[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_byId_1ea48d81-8019-4294-819c-cf3209fb5207":3,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:1ea48d81-8019-4294-819c-cf3209fb5207,\"}":105},{"code":4,"data":5,"meta":18},"SUCCESS",{"id":6,"createTime":7,"updateTime":8,"relativeEntities":9,"slug":10,"properties":11,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":20,"manageAffiliations":39,"indexDatabases":54,"url":91,"thumbnailPath":18,"statistic":92,"gsStatistic":18,"type":104,"analyzePriority":18},"1ea48d81-8019-4294-819c-cf3209fb5207","2024-04-17T10:40:37.533+00:00","2025-11-21T10:03:39.281+00:00",[],"BMC-Medical-Informatics-and-Decision-Making",{"issn":12,"title":14},{"VOID":13},"1472-6947",{"EN":15},"BMC Medical Informatics and Decision Making","PUBLISHER","PENDING",null,0,[21,27,33],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":23,"label":24,"description":26,"parentId":18,"standard":18,"scholarHubFieldId":18},"ff8a5e2d-70c7-48e6-a301-4d13b455897a",[],{"EN":25},"Health Informatics",{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":29,"label":30,"description":32,"parentId":18,"standard":18,"scholarHubFieldId":18},"8a494f62-57a5-4668-b75a-4307e7d55da0",[],{"EN":31},"Health Policy",{},{"id":34,"createTime":18,"updateTime":18,"relativeEntities":35,"label":36,"description":38,"parentId":18,"standard":18,"scholarHubFieldId":18},"bb1bad44-29ec-44f6-a988-069cb69215fd",[],{"EN":37},"Computer Science Applications",{},[40,47],{"id":41,"createTime":18,"updateTime":18,"relativeEntities":42,"slug":18,"properties":43,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":46,"statistic":18},"67883518-0c98-470e-b6b0-160ab49bb03d",[],{"title":44},{"EN":45},"BMC",[],{"id":48,"createTime":18,"updateTime":18,"relativeEntities":49,"slug":18,"properties":50,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":53,"statistic":18},"c5894808-4e99-4047-bbce-594f58821845",[],{"title":51},{"EN":52},"BioMed Central Ltd.",[],[55,74],{"id":56,"indexDatabase":57,"url":67,"indexYears":68,"academicFieldIds":69,"indexDatabaseRanking":73},"0bbc3f28-b1f9-4612-8662-8fc73f71463b",{"id":58,"createTime":18,"updateTime":18,"relativeEntities":59,"label":60,"description":62,"key":64,"publicationTags":65,"standard":18},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9",[],{"EN":61,"VI":61},"Scopus - Elsevier",{"EN":61,"VI":63},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[66],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F23602","2001-2025",[70,71,72],"212b8bab-be53-49b1-9ceb-c528868800fc","5901a6f0-93ba-4108-a5df-b363e796a7cf","c988b05f-b47d-441e-b68f-0d781b8bc15f","SCOPUS__Q1",{"id":75,"indexDatabase":76,"url":88,"indexYears":18,"academicFieldIds":89,"indexDatabaseRanking":18},"cd089623-4b9e-4dc8-a1dd-0bfa024ae5c2",{"id":77,"createTime":18,"updateTime":18,"relativeEntities":78,"label":79,"description":81,"key":84,"publicationTags":85,"standard":18},"a4921856-b128-4d9f-8f1f-e80813d3bbd4",[],{"EN":80,"VI":80},"ISI\u002FSCIE - Science Citation Index Expanded",{"EN":82,"VI":83},"SCIE database","Cơ sở dữ liệu SCIE","scie",[86,87],"SCIE","ISI","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002Fnull",[90],"a5700e6c-c8d0-4cb1-9fee-49b861c37103","https:\u002F\u002Flink.springer.com\u002Fjournal\u002F12911",{"impactFactor":19,"impactFactorByYear":93,"i10Index":96,"i10IndexLast5Year":19,"totalPublication":97,"totalPublicationByYear":98,"totalCitation":100,"totalCitationByYear":101,"totalCitationPerPublication":102,"totalCitationPerPublicationByYear":103,"hindexLast5Year":96,"hindex":96},{"2010":94,"2011":95},2,3,1,30,{"2007":96,"2009":96,"2011":96,"2012":96,"2013":94,"2014":94,"2016":94,"2017":94,"2019":95,"2020":95,"2021":99,"2022":95,"2024":94},7,32,{"2009":100},1.07,{"2009":100},"JOURNAL",{"meta":106,"data":108},{"total":107},"2235",[109,280,429,813,1323,1609,1722,2061,2256,2642],{"id":110,"createTime":111,"updateTime":112,"relativeEntities":113,"slug":114,"properties":115,"entityType":125,"verifyStatus":126,"verifyTime":127,"verifyNote":128,"languages":18,"translateLanguages":129,"viewCount":19,"primaryUrl":131,"fullTextUrl":18,"authors":132,"publicationType":220,"publisherRelationship":221,"citationCount":18,"citationInfo":18,"publishDate":276,"publishYear":277,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":278,"openAccess":18,"references":18,"isForceReanalyzing":279},"60404d24-2e32-4e7b-acc9-52603e13cce4","2023-12-18T01:24:21.958+00:00","2026-09-11T03:11:02.665+00:00",[],"Simulating-an-emergency-department-the-importance-of-modeling-the-interactions-between-physicians-and-delegates-in-a-discrete-event-simulation",{"abstract":116,"title":118,"references":121,"doi":123},{"EN":117},"Computer simulation studies of the emergency department (ED) are often patient driven and consider the physician as a human resource whose primary activity is interacting directly with the patient. In many EDs, physicians supervise delegates such as residents, physician assistants and nurse practitioners each with different skill sets and levels of independence. The purpose of this study is to present an alternative approach where physicians and their delegates in the ED are modeled as interacting pseudo-agents in a discrete event simulation (DES) and to compare it with the traditional approach ignoring such interactions. The new approach models a hierarchy of heterogeneous interacting pseudo-agents in a DES, where pseudo-agents are entities with embedded decision logic. The pseudo-agents represent a physician and delegate, where the physician plays a senior role to the delegate (i.e. treats high acuity patients and acts as a consult for the delegate). A simple model without the complexity of the ED is first created in order to validate the building blocks (programming) used to create the pseudo-agents and their interaction (i.e. consultation). Following validation, the new approach is implemented in an ED model using data from an Ontario hospital. Outputs from this model are compared with outputs from the ED model without the interacting pseudo-agents. They are compared based on physician and delegate utilization, patient waiting time for treatment, and average length of stay. Additionally, we conduct sensitivity analyses on key parameters in the model. In the hospital ED model, comparisons between the approach with interaction and without showed physician utilization increase from 23% to 41% and delegate utilization increase from 56% to 71%. Results show statistically significant mean time differences for low acuity patients between models. Interaction time between physician and delegate results in increased ED length of stay and longer waits for beds. This example shows the importance of accurately modeling physician relationships and the roles in which they treat patients. Neglecting these relationships could lead to inefficient resource allocation due to inaccurate estimates of physician and delegate time spent on patient related activities and length of stay.",{"EN":119,"VI":120},"Simulating an emergency department: the importance of modeling the interactions between physicians and delegates in a discrete event simulation","Mô phỏng khoa cấp cứu: tầm quan trọng của việc mô hình hóa các tương tác giữa bác sĩ và nhân viên được ủy thác trong mô phỏng sự kiện rời rạc",{"VOID":122},"Lim M, Nye T, Bowen J, Hurley J, Goeree R, Tarride JE: Mathematical modeling: the case of emergency department waiting times. Int J Technol Assess Health Care. 2012, 28 (2): 93-109. 10.1017\u002FS0266462312000013.\nHay A, Valentin EC, Bijlsma RA: Modeling emergency care in hospitals: a paradox - the patient should not drive the process. Proceedings of the 2006 Winter Simulation Conference. 2006, 439-445.\nRaunak M, Osterweil L, Wise A, Clarke L, Henneman P: Simulating patient flow through an emergency department using process-driven discrete event simulation. 2009 ICSE Workshop on Software Engineering in Health Care (SEHC 2009), 18–19 May 2009. 2009, 73-83.\nGunal M, Pidd M: Understanding accident and emergency department performance using simulation. Proceedings of the 2006 Winter Simulation Conference. 2006, 446-452.\nKomashie A, Mousavi A: Modeling emergency departments using discrete event simulation techniques. Proceedings of the 2005 Winter Simulation Conference. 2005, 2681-2685.\nMahapatra S, Koellig C, Patvivatsiri L, Fraticelli B, Eitel D, Grove L: Pairing emergency severity index level triage data with computer aided system design to improve emergency department access and throughput. Proceedings of the 2003 Winter Simulation Conference. 2003, 1917-1925.\nSantibanez P, Chow VS, French J, Martin PL, Tyldesley S: Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency’s ambulatory care unit through simulation. Health Care Manag Sci. 2009, 12: 392-407. 10.1007\u002Fs10729-009-9103-1.\nLammers RL, Roiger M, Rice L, Overton DT, Cucos D: The effect of a new emergency medicine residency program on patient length of stay in a community hospital emergency department. Acad Emerg Med. 2003, 10 (7): 725-730. 10.1111\u002Fj.1553-2712.2003.tb00066.x.\nSalazar A, Corbella X, Onaga H, Ramon R, Pallares R, Escarrabill J: Impact of a resident strike on emergency department quality indicators at an urban teaching hospital. Acad Emerg Med. 2001, 8 (8): 804-808. 10.1111\u002Fj.1553-2712.2001.tb00210.x.\nBush SH, Lao MR, Simmons KL, Goode JH, Cunningham SA, Calhoun BC: Patient access and clinical efficiency improvement in a resident hospital-based women’s medicine center clinic. Am J Manag Care. 2007, 13 (12): 686-690.\nShayne P, Lin M, Ufberg JW, Ankel F, Barringer K, Morgan-Edwards S: The effect of emergency department crowding on education: blessing or curse?. Acad Emerg Med. 2009, 16 (1): 76-82. 10.1111\u002Fj.1553-2712.2008.00261.x.\nChisholm CD, Whenmouth LF, Daly EA, Cordell WH, Giles BK, Brizendine EJ: An evaluation of emergency medicine resident interaction time with faculty in different teaching venues. Acad Emerg Med. 2004, 11 (2): 149-155.\nHollingsworth JC, Chisholm CD, Giles BK, Cordell WH, Nelson DR: How do physicians and nurses spend their time in the emergency department?. Ann Emerg Med. 1998, 31 (1): 87-91. 10.1016\u002FS0196-0644(98)70287-2.\nSiebers PO, Macal CM, Garnett J, Buxton D, Pidd M: Discrete-event simulation is dead, long live agent-based simulation!. Journal of Simulation. 2010, 4: 204-210. 10.1057\u002Fjos.2010.14.\nJones SS, Evans RS: An agent based simulation tool for scheduling emergency department physicians. AMIA Annual Symposium Proceedings. 2008, 338-342.\nLaskowski M, McLeod RD, Friesen MR, Podaima BW, Alfa AS: Models of emergency departments for reducing patient waiting times. PLoS ONE [Electronic Resource]. 2009, 4 (7): e6127-10.1371\u002Fjournal.pone.0006127.\nWang L: An agent-based simulation for workflow. Proceedings of the 2009 IEEE Systems and Information. 2009, 19-23.\nTaboada M, Cabrera E, Iglesias ML, Epelde F, Luque E: An agent-based decision support system for hospitals emergency departments. Procedia Comput Sci. 2011, 203 (4): 1870-1879.\nMichel F, Ferber J, Drogoul A: Multi-Agent Systems and Simulation: A Survey of the Agent Community’s Perspective. Edited by: Uhrmacher AM, Weyns D. 2009, Boca Raton, FL, USA: Multi-Agent Systems: Simulation and Applications CRC Press. Taylor and Francis Group, 3-52.\nKarnon J, Stahl J, Brennan A, Caro JJ, Mar J, Möller J: Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–4. Value in Health. 2012, 15 (6): 821-827. 10.1016\u002Fj.jval.2012.04.013.\nBeveridge B, Clarke B, Janes L, Savage N, Thompson J, Dodd G, Murray M, Nijssen-Jordan C, Vadeboncoeur A, Canadian Association of Emergency Physicians: CTAS Implementation Guidelines. Accessed on March 21, 2012. http:\u002F\u002Fcaep.ca\u002Fresources\u002Fctas\u002Fimplementation-guidelines\nHeidelberger P, Welch P: Simulation run length control in the presence of an initial transient. Oper Res. 1983, 31 (6): 1109-1144. 10.1287\u002Fopre.31.6.1109.\nKelton WD, Sadowski RP, Sturrock DT: Simulation with Arena. 4. 2007, New York: McGraw Hill\nFriedman S, Elinson R, Arenovich T: A study of emergency physician work and communication: a human factors approach. Israeli J Emerg Med. 2005, 5 (3): 35-42.\nThe pre-publication history for this paper can be accessed here:http:\u002F\u002Fwww.biomedcentral.com\u002F1472-6947\u002F13\u002F59\u002Fprepub",{"VOID":124},"10.1186\u002F1472-6947-13-59","PUBLICATION","VERIFIED","2025-02-13T00:23:21.350+00:00","Auto Verify",[130],"VI","https:\u002F\u002Fbmcmedinformdecismak.biomedcentral.com\u002Farticles\u002F10.1186\u002F1472-6947-13-59",[133,158,180,200],{"id":134,"sortIndex":19,"researcher":18,"roles":135,"affiliations":137,"properties":155,"displayName":157,"givenName":18,"familyName":18},"a12d0147-d7ba-4f30-b719-c60662f48f2f",[136],"AUTHOR",[138,146],{"id":139,"sortIndex":19,"affiliation":140,"properties":18},"7e69783d-2a11-483b-a199-b1df84c64434",{"id":139,"createTime":18,"updateTime":18,"relativeEntities":141,"slug":18,"properties":142,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":145,"statistic":18},[],{"title":143},{"VI":144},"Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada",[],{"id":147,"sortIndex":96,"affiliation":148,"properties":154},"3349e869-53f3-473b-b1ce-4854bc2fe37a",{"id":147,"createTime":18,"updateTime":18,"relativeEntities":149,"slug":18,"properties":150,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":153,"statistic":18},[],{"title":151},{"VI":152},"Programs for Assessment of Technology in Health (PATH) Research Institute, St. Joseph's Healthcare Hamilton, Hamilton, Canada",[],{},{"title":156},{"VI":157},"Morgan E Lim",{"id":159,"sortIndex":96,"researcher":18,"roles":160,"affiliations":161,"properties":177,"displayName":179,"givenName":18,"familyName":18},"d0f62da7-c185-4547-ba5e-16537053a141",[136],[162,168],{"id":139,"sortIndex":19,"affiliation":163,"properties":18},{"id":139,"createTime":18,"updateTime":18,"relativeEntities":164,"slug":18,"properties":165,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":167,"statistic":18},[],{"title":166},{"VI":144},[],{"id":169,"sortIndex":96,"affiliation":170,"properties":176},"c1563f99-a4e2-45ff-9df8-ed1e91ada10a",{"id":169,"createTime":18,"updateTime":18,"relativeEntities":171,"slug":18,"properties":172,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":175,"statistic":18},[],{"title":173},{"VI":174},"Department of Medicine, Division of Emergency Medicine, McMaster University, Ontario, Canada",[],{},{"title":178},{"VI":179},"Andrew Worster",{"id":181,"sortIndex":94,"researcher":18,"roles":182,"affiliations":183,"properties":197,"displayName":199,"givenName":18,"familyName":18},"20768a84-24e5-4811-8d57-6bf4980472a4",[136],[184,190],{"id":139,"sortIndex":19,"affiliation":185,"properties":18},{"id":139,"createTime":18,"updateTime":18,"relativeEntities":186,"slug":18,"properties":187,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":189,"statistic":18},[],{"title":188},{"VI":144},[],{"id":147,"sortIndex":96,"affiliation":191,"properties":196},{"id":147,"createTime":18,"updateTime":18,"relativeEntities":192,"slug":18,"properties":193,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":195,"statistic":18},[],{"title":194},{"VI":152},[],{},{"title":198},{"VI":199},"Ron 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Tarride","ARTICLE",{"url":131,"publisher":222,"properties":271},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":223,"slug":10,"properties":224,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":227,"manageAffiliations":240,"indexDatabases":251,"url":91,"thumbnailPath":18,"statistic":266,"gsStatistic":18,"type":104,"analyzePriority":18},[],{"issn":225,"title":226},{"VOID":13},{"EN":15},[228,232,236],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":229,"label":230,"description":231,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":233,"label":234,"description":235,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},{"id":34,"createTime":18,"updateTime":18,"relativeEntities":237,"label":238,"description":239,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":37},{},[241,246],{"id":41,"createTime":18,"updateTime":18,"relativeEntities":242,"slug":18,"properties":243,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":245,"statistic":18},[],{"title":244},{"EN":45},[],{"id":48,"createTime":18,"updateTime":18,"relativeEntities":247,"slug":18,"properties":248,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":250,"statistic":18},[],{"title":249},{"EN":52},[],[252,259],{"id":56,"indexDatabase":253,"url":67,"indexYears":68,"academicFieldIds":258,"indexDatabaseRanking":73},{"id":58,"createTime":18,"updateTime":18,"relativeEntities":254,"label":255,"description":256,"key":64,"publicationTags":257,"standard":18},[],{"EN":61,"VI":61},{"EN":61,"VI":63},[66],[70,71,72],{"id":75,"indexDatabase":260,"url":88,"indexYears":18,"academicFieldIds":265,"indexDatabaseRanking":18},{"id":77,"createTime":18,"updateTime":18,"relativeEntities":261,"label":262,"description":263,"key":84,"publicationTags":264,"standard":18},[],{"EN":80,"VI":80},{"EN":82,"VI":83},[86,87],[90],{"impactFactor":19,"impactFactorByYear":267,"i10Index":96,"i10IndexLast5Year":19,"totalPublication":97,"totalPublicationByYear":268,"totalCitation":100,"totalCitationByYear":269,"totalCitationPerPublication":102,"totalCitationPerPublicationByYear":270,"hindexLast5Year":96,"hindex":96},{"2010":94,"2011":95},{"2007":96,"2009":96,"2011":96,"2012":96,"2013":94,"2014":94,"2016":94,"2017":94,"2019":95,"2020":95,"2021":99,"2022":95,"2024":94},{"2009":100},{"2009":100},{"pages":272,"volume":274},{"VOID":273},"1-11",{"VOID":275},"13","2013-05-22",2013,[86,73],false,{"id":281,"createTime":282,"updateTime":283,"relativeEntities":284,"slug":285,"properties":286,"entityType":125,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":296,"viewCount":19,"primaryUrl":297,"fullTextUrl":18,"authors":298,"publicationType":220,"publisherRelationship":371,"citationCount":18,"citationInfo":18,"publishDate":426,"publishYear":427,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":428,"openAccess":18,"references":18,"isForceReanalyzing":279},"8a197f6d-fa88-4ff4-b83f-39e0e9f9417f","2023-12-31T20:10:19.109+00:00","2026-09-09T12:13:43.345+00:00",[],"Computational-Barthel-Index-an-automated-tool-for-assessing-and-predicting-activities-of-daily-living-among-nursing-home-patients",{"abstract":287,"title":289,"references":292,"doi":294},{"EN":288},"Assessment of functional ability, including activities of daily living (ADLs), is a manual process completed by skilled health professionals. In the presented research, an automated decision support tool, the Computational Barthel Index Tool (CBIT), was constructed that can automatically assess and predict probabilities of current and future ADLs based on patients’ medical history. The data used to construct the tool include the demographic information, inpatient and outpatient diagnosis codes, and reported disabilities of 181,213 residents of the Department of Veterans Affairs’ (VA) Community Living Centers. Supervised machine learning methods were applied to construct the CBIT. Temporal information about times from the first and the most recent occurrence of diagnoses was encoded. Ten-fold cross-validation was used to tune hyperparameters, and independent test sets were used to evaluate models using AUC, accuracy, recall and precision. Random forest achieved the best model quality. Models were calibrated using isotonic regression. The unabridged version of CBIT uses 578 patient characteristics and achieved average AUC of 0.94 (0.93–0.95), accuracy of 0.90 (0.89–0.91), precision of 0.91 (0.89–0.92), and recall of 0.90 (0.84–0.95) when re-evaluating patients. CBIT is also capable of predicting ADLs up to one year ahead, with accuracy decreasing over time, giving average AUC of 0.77 (0.73–0.79), accuracy of 0.73 (0.69–0.80), precision of 0.74 (0.66–0.81), and recall of 0.69 (0.34–0.96). A simplified version of CBIT with 50 top patient characteristics reached performance that does not significantly differ from full CBIT. Discharge planners, disability application reviewers and clinicians evaluating comparative effectiveness of treatments can use CBIT to assess and predict information on functional status of patients.",{"EN":290,"VI":291},"Computational Barthel Index: an automated tool for assessing and predicting activities of daily living among nursing home patients","Thang điểm Barthel tính toán: Công cụ tự động đánh giá và dự đoán các hoạt động sinh hoạt hàng ngày ở bệnh nhân tại viện dưỡng lão",{"VOID":293},"Fried TR, Bradley EH, Towle VR, Allore H. Understanding the treatment preferences of seriously ill patients. N Engl J Med. 2002;346(14):1061–6.\nMcCarthy EP, Phillips RS, Zhong Z, Drews RE, Lynn J. Dying with cancer: patients’ function, symptoms, and care preferences as death approaches. J Am Geriatr Soc. 2000;48(S1):S110–21.\nMDS 3.0 Technical Information. https:\u002F\u002Fwww.cms.gov\u002FMedicare\u002FQuality-Initiatives-Patient-Assessment-Instruments\u002FNursingHomeQualityInits\u002FNHQIMDS30TechnicalInformation.\nCollin C, Wade DT, Davies S, Horne V. The Barthel ADL Index: a reliability study. Int Disabil Stud. 1988;10(2):61–3.\nShah S, Vanclay F, Cooper B. Improving the sensitivity of the Barthel Index for stroke rehabilitation. J Clin Epidemiol. 1989;42(8):703–9.\nTHE BARTHEL INDEX. Strokecenter.org. [cited 2020 Nov 6]. http:\u002F\u002Fwww.strokecenter.org\u002Fwp-content\u002Fuploads\u002F2011\u002F08\u002Fbarthel.pdf\nDy SM, Pfoh ER, Salive ME, Boyd CM. Health-related quality of life and functional status quality indicators for older persons with multiple chronic conditions. J Am Geriatr Soc. 2013;61(12):2120–7.\nWojtusiak J, Levy CR, Williams AE, Alemi F. Predicting functional decline and recovery for residents in veterans affairs nursing homes. Gerontologist. 2016;56(1):42–51.\nLevy CR, Zargoush M, Williams AE, Williams AR, Giang P, Wojtusiak J, Kheirbek RE, Alemi F. Sequence of functional loss and recovery in nursing homes. Gerontologist. 2016;56(1):52–61.\nMahoney FI, Barthel DW. Functional evaluation: the Barthel Index: a simple index of independence useful in scoring improvement in the rehabilitation of the chronically ill. Maryland State Med J. 1965;14:61–5.\nHong HG, An HS, Sarzynski E, Oberst K. New composite measure for ADL limitations: application to predicting nursing home placement for Michigan MI choice clients. Med Care Res Rev 2019:1077558719886735.\nLi QX, Zhao XJ, Wang Y, Wang DL, Zhang J, Liu TJ, Peng YB, Fan HY, Zheng FX. Value of the Barthel scale in prognostic prediction for patients with cerebral infarction. BMC Cardiovasc Disord. 2020;20(1):1–5.\nVeerbeek JM, Kwakkel G, van Wegen EE, Ket JC, Heymans MW. Early prediction of outcome of activities of daily living after stroke: a systematic review. 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IEEE.\nLiu J, Sohn J, Kim S. Classification of daily activities for the elderly using wearable sensors. J Healthc Eng. 2017;2017:8934816.\nCook DJ, Schmitter-Edgecombe M, Jönsson L, Morant AV. Technology-enabled assessment of functional health. IEEE Rev Biomed Eng. 2018;12:319–32.\nChatterjee P, Armentano R, Palombi L, Kun L. Editorial preface: Special issue on IoT for eHealth, elderly and aging. Internet Things. 2019. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.iot.2019.100115.\nAkbari A, Jafari R. Personalizing activity recognition models with quantifying different types of uncertainty using wearable sensors. IEEE Trans Biomed Eng. 2020;67(9):2530–41.\nSridharan M, Bigham J, Campbell PM, Phillips C, Bodanese E. Inferring micro-activities using wearable sensing for ADL recognition of home-care patients. IEEE J Biomed Health Inform. 2019;24(3):747–59.\nRobben S, Englebienne G, Kröse B. Delta features from ambient sensor data are good predictors of change in functional health. IEEE J Biomed Health Inform. 2016;21(4):986–93.\nGhayvat H, Mukhopadhyay S, Shenjie B, Chouhan A, Chen W. Smart home based ambient assisted living: Recognition of anomaly in the activity of daily living for an elderly living alone. In: 2018 IEEE international instrumentation and measurement technology conference (I2MTC) 2018, pp. 1–5. IEEE.\nSasaki W, Fujiwara M, Fujimoto M, Suwa H, Arakawa Y, Yasumoto K. Predicting occurrence time of daily living activities through time series analysis of smart home data. In: 2019 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops) 2019, pp. 233–238. IEEE.\nSokullu R, Akkaş MA, Demir E. IoT Supported smart home for the elderly. Internet of Things 2020:100239.\nDhiman C, Vishwakarma DK. A review of state-of-the-art techniques for abnormal human activity recognition. Eng Appl Artif Intell. 2019;77:21–45.\nHussain Z, Sheng QZ, Zhang WE. A review and categorization of techniques on device-free human activity recognition. J Netw Comput Appl. 2020;167:102738.\nNizar Banu PK, Kavitha R. Single activity recognition system: a review. In: Alam M, Shakil KA, Khan S, editors. Internet of Things (IoT). Cham: Springer; 2020. p. 257–71.\nLevy CR, Alemi F, Williams AE, Williams AR, Wojtusiak J, Sutton B, Giang P, Pracht E, Argyros L. Shared homes as an alternative to nursing home care: Impact of VA’s medical foster home program on hospitalization. Gerontologist. 2016;56(1):62–71.\nHawes C, Morris JN, Phillips CD, Mor V, Fries BE, Nonemaker S. Reliability estimates for the Minimum Data Set for nursing home resident assessment and care screening (MDS). Gerontologist. 1995;35(2):172–8.\nHandelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eD octor: machine learning and the future of medicine. J Intern Med. 2018;284(6):603–19.\nBreiman L. Random forests. Mach Learn. 2001;45(1):5–32.\nBreiman L. Bagging predictors. Mach Learn. 1996;24(2):123–40.\nOlson MA, Wyner AJ. Making sense of random forest probabilities: a kernel perspective. arXiv preprint arXiv:1812.05792. 2018.\nPedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.\nMatplotlib: Python plotting — Matplotlib 3.2.2 documentation. [cited 2020 Jun 25]. https:\u002F\u002Fmatplotlib.org\u002F\nWojtusiak J. Machine Learning and Inference Reporting Criteria. Reports of the Machine Learning and Inference Laboratory, MLI 20–1.2020.\nComputational Barthel Index (CBIT) for Activities of Daily Living. [cited 2020 Jun 25]. https:\u002F\u002Fhi.gmu.edu\u002Fcbit.\nStenholm S, Westerlund H, Salo P, Hyde M, Pentti J, Head J, Kivimäki M, Vahtera J. Age-related trajectories of physical functioning in work and retirement: the role of sociodemographic factors, lifestyle and disease. J Epidemiol Community Health. 2014;68(6):503–9.\nNisar MA, Shirahama K, Li F, Huang X, Grzegorzek M. Rank pooling approach for wearable sensor-based ADLs recognition. Sensors. 2020;20(12):3463.\nPoli A, Scalise L, Spinsante S, Strazza A. ADLs Monitoring by accelerometer-based wearable sensors: effect of measurement device and data uncertainty on classification accuracy. In: 2020 IEEE international symposium on medical measurements and applications (MeMeA) 2020, pp. 1–6. IEEE.\nVepakomma P, De D, Das SK, Bhansali S. A-Wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. In: 2015 IEEE 12th International conference on wearable and implantable body sensor networks (BSN) 2015 Jun 9 (pp. 1–6). IEEE.",{"VOID":295},"10.1186\u002Fs12911-020-01368-8",[130],"https:\u002F\u002Fbmcmedinformdecismak.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12911-020-01368-8",[299,314,327,342,355],{"id":300,"sortIndex":19,"researcher":18,"roles":301,"affiliations":302,"properties":311,"displayName":313,"givenName":18,"familyName":18},"49ffa9b8-2249-4089-95d7-c0d041effa85",[136],[303],{"id":304,"sortIndex":19,"affiliation":305,"properties":18},"3e4e78a6-4914-4964-b52e-9705f1b13cf6",{"id":304,"createTime":18,"updateTime":18,"relativeEntities":306,"slug":18,"properties":307,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":310,"statistic":18},[],{"title":308},{"VI":309},"Health Informatics Program, Department of Health Administration and Policy, George Mason University, Fairfax, USA",[],{"title":312},{"VI":313},"Janusz Wojtusiak",{"id":315,"sortIndex":96,"researcher":18,"roles":316,"affiliations":317,"properties":324,"displayName":326,"givenName":18,"familyName":18},"7a070967-8810-43d5-aa5a-0b9448976c6f",[136],[318],{"id":304,"sortIndex":19,"affiliation":319,"properties":18},{"id":304,"createTime":18,"updateTime":18,"relativeEntities":320,"slug":18,"properties":321,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":323,"statistic":18},[],{"title":322},{"VI":309},[],{"title":325},{"VI":326},"Negin Asadzadehzanjani",{"id":328,"sortIndex":94,"researcher":18,"roles":329,"affiliations":330,"properties":339,"displayName":341,"givenName":18,"familyName":18},"601df61c-9cab-4845-8611-ccf07b0241de",[136],[331],{"id":332,"sortIndex":19,"affiliation":333,"properties":18},"62a57d0d-eb7b-44ef-b135-c23ee9bd2475",{"id":332,"createTime":18,"updateTime":18,"relativeEntities":334,"slug":18,"properties":335,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":338,"statistic":18},[],{"title":336},{"VI":337},"Department of Veterans Affairs, Denver, USA",[],{"title":340},{"VI":341},"Cari Levy",{"id":343,"sortIndex":95,"researcher":18,"roles":344,"affiliations":345,"properties":352,"displayName":354,"givenName":18,"familyName":18},"173285cb-baf0-471b-948f-ba119c67c1b8",[136],[346],{"id":304,"sortIndex":19,"affiliation":347,"properties":18},{"id":304,"createTime":18,"updateTime":18,"relativeEntities":348,"slug":18,"properties":349,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":351,"statistic":18},[],{"title":350},{"VI":309},[],{"title":353},{"VI":354},"Farrokh Alemi",{"id":356,"sortIndex":357,"researcher":18,"roles":358,"affiliations":359,"properties":368,"displayName":370,"givenName":18,"familyName":18},"ba1aaeb6-54c0-4ffd-b774-156e915dfb35",4,[136],[360],{"id":361,"sortIndex":19,"affiliation":362,"properties":18},"9cce7182-4c06-4b0f-beb1-6cb9ac1c5b24",{"id":361,"createTime":18,"updateTime":18,"relativeEntities":363,"slug":18,"properties":364,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":367,"statistic":18},[],{"title":365},{"VI":366},"Department of Veterans Affairs, Bay Pines, USA",[],{"title":369},{"VI":370},"Allison E. 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Prediabetes increases the risk of CVD, which is a leading cause of death in the United States. CVD clinical decision support (CDS) in primary care settings has the potential to reduce cardiovascular risk in patients with prediabetes while potentially saving clinicians time. The objective of this study is to understand primary care clinician (PCC) perceptions of a CDS system designed to reduce CVD risk in adults with prediabetes.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Methods\u003C\u002Fjats:title>\n                \u003Cjats:p>We administered pre-CDS implementation (6\u002F30\u002F2016 to 8\u002F25\u002F2016) (n = 183, 61% response rate) and post-CDS implementation (6\u002F12\u002F2019 to 8\u002F7\u002F2019) (n = 131, 44.5% response rate) independent cross-sectional electronic surveys to PCCs at 36 randomized primary care clinics participating in a federally funded study of a CVD risk reduction CDS tool. Surveys assessed PCC demographics, experiences in delivering prediabetes care, perceptions of CDS impact on shared decision making, perception of CDS impact on control of major CVD risk factors, and overall perceptions of the CDS tool when managing cardiovascular risk.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Results\u003C\u002Fjats:title>\n                \u003Cjats:p>We found few significant differences when comparing pre- and post-implementation responses across CDS intervention and usual care (UC) clinics. A majority of PCCs felt well-prepared to discuss CVD risk factor control with patients both pre- and post-implementation. About 73% of PCCs at CDS intervention clinics agreed that the CDS helped improve risk control, 68% reported the CDS added value to patient clinic visits, and 72% reported they would recommend use of this CDS system to colleagues. However, most PCCs disagreed that the CDS saves time talking about preventing diabetes or CVD, and most PCCs also did not find the clinical domains useful, nor did PCCs believe that the clinical domains were useful in getting patients to take action. Finally, only about 38% reported they were satisfied with the CDS.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Conclusions\u003C\u002Fjats:title>\n                \u003Cjats:p>These results improve our understanding of CDS user experience and can be used to guide iterative improvement of the CDS. While most PCCs agreed the CDS improves CVD and diabetes risk factor control, they were generally not satisfied with the CDS. Moreover, only 40–50% agreed that specific suggestions on clinical domains helped patients to take action. In spite of this, an overwhelming majority reported they would recommend the CDS to colleagues, pointing for the need to improve upon the current CDS.\u003C\u002Fjats:p>\n                \u003Cjats:p>\u003Cjats:italic>Trial registration\u003C\u002Fjats:italic>: NCT02759055 03\u002F05\u002F2016.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>",{"EN":441,"VI":442},"Clinician perceptions of a clinical decision support system to reduce cardiovascular risk among prediabetes patients in a predominantly rural healthcare system","Nhận thức của nhân viên y tế về một hệ thống hỗ trợ quyết định lâm sàng nhằm giảm nguy cơ tim mạch ở bệnh nhân tiền đái tháo đường trong hệ thống y tế chủ yếu ở vùng nông thôn",{"VOID":444},"36402988",{"VOID":446},"10.1186\u002Fs12911-022-02032-z","2025-02-18T21:29:41.640+00:00",[449],"EN",[130],"https:\u002F\u002Fbmcmedinformdecismak.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12911-022-02032-z",[453,470,487,502,519,538,556,572,587,603,619],{"id":454,"sortIndex":19,"researcher":18,"roles":455,"affiliations":456,"properties":465,"displayName":467,"givenName":18,"familyName":18},"9beff46a-357a-42ca-8793-6745e9a0094d",[],[457],{"id":458,"sortIndex":19,"affiliation":459,"properties":18},"863a243b-e772-4a99-9469-6adc5ff35659",{"id":458,"createTime":18,"updateTime":18,"relativeEntities":460,"slug":18,"properties":461,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":464,"statistic":18},[],{"title":462},{"EN":463},"Carle Foundation Hospital Clinical Business and Intelligence, 611 W Park Street, Urbana, IL, 61801, USA",[],{"title":466,"openalex":468},{"EN":467},"Daniel M. 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The surprising truth about prediabetes 2020. Available from: https:\u002F\u002Fwww.cdc.gov\u002Fdiabetes\u002Flibrary\u002Ffeatures\u002Ftruth-about-prediabetes.html.",{},{"id":18,"text":695,"url":18,"identifiers":696},"Hostalek U. Global epidemiology of prediabetes - present and future perspectives. Clin Diabetes Endocrinol. 2019;5:5.",{"doi":697},"10.1186\u002Fs40842-019-0080-0",{"id":18,"text":699,"url":18,"identifiers":700},"Huang Y, Cai X, Mai W, Li M, Hu Y. Association between prediabetes and risk of cardiovascular disease and all cause mortality: systematic review and meta-analysis. BMJ. 2016;355:i5953.",{"doi":701},"10.1136\u002Fbmj.i5953",{"id":18,"text":703,"url":18,"identifiers":704},"Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, et al. Effect of clinical decision-support systems: a systematic review. 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Understanding primary care providers’ perceptions of cancer prevention and screening in a predominantly rural healthcare system in the upper Midwest. BMC Health Serv Res. 2019;19(1):1019.",{"doi":717},"10.1186\u002Fs12913-019-4872-9",{"id":18,"text":719,"url":18,"identifiers":720},"Kandula NR, Moran MR, Tang JW, O’Brien MJ. Preventing diabetes in primary care: providers’ perspectives about diagnosing and treating prediabetes. Clin Diabetes. 2018;36(1):59–66.",{"doi":721},"10.2337\u002Fcd17-0049",{"id":18,"text":723,"url":18,"identifiers":724},"Bhuyan SS, Chandak A, Gupta N, Isharwal S, LaGrange C, Mahmood A, et al. Patient-provider communication about prostate cancer screening and treatment: new evidence from the health information national trends survey. Am J Mens Health. 2017;11(1):134–46.",{"doi":725},"10.1177\u002F1557988315614082",{"id":18,"text":727,"url":18,"identifiers":728},"Dunn AS, Shridharani KV, Lou W, Bernstein J, Horowitz CR. Physician-patient discussions of controversial cancer screening tests. Am J Prev Med. 2001;20(2):130–4.",{"doi":729},"10.1016\u002FS0749-3797(00)00288-9",{"id":18,"text":731,"url":18,"identifiers":732},"Guerra CE, Jacobs SE, Holmes JH, Shea JA. Are physicians discussing prostate cancer screening with their patients and why or why not? A pilot study. J Gen Intern Med. 2007;22(7):901–7.",{"doi":733},"10.1007\u002Fs11606-007-0142-3",{"id":18,"text":735,"url":18,"identifiers":736},"Jia P, Zhang L, Chen J, Zhao P, Zhang M. The effects of clinical decision support systems on medication safety: an overview. PLOS ONE. 2016;11(12):e0167683.",{"doi":737},"10.1371\u002Fjournal.pone.0167683",{"id":18,"text":739,"url":18,"identifiers":740},"Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17.",{"doi":741},"10.1038\u002Fs41746-020-0221-y",{"id":18,"text":743,"url":18,"identifiers":744},"Saleem JJ, Militello LG, Arbuckle N, Flanagan M, Haggstrom DA, Linder JA, et al. Provider perceptions of colorectal cancer screening clinical decision support at three benchmark institutions. In: AMIA Annual Symposium Proceedings. 2009. p. 558–62.",{},{"id":18,"text":746,"url":18,"identifiers":747},"Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA. 1998;280(15):1339–46.",{"doi":748},"10.1001\u002Fjama.280.15.1339",{"id":18,"text":750,"url":18,"identifiers":751},"Gonzalez ER, Vanderheyden BA, Ornato JP, Comstock TG. Computer-assisted optimization of aminophylline therapy in the emergency department. Am J Emerg Med. 1989;7(4):395–401.",{"doi":752},"10.1016\u002F0735-6757(89)90046-6",{"id":18,"text":754,"url":18,"identifiers":755},"Han PK, Kobrin S, Breen N, Joseph DA, Li J, Frosch DL, et al. National evidence on the use of shared decision making in prostate-specific antigen screening. Ann Fam Med. 2013;11(4):306–14.",{"doi":756},"10.1370\u002Fafm.1539",{"id":18,"text":758,"url":18,"identifiers":759},"Marc DT, Khairat SS. Why do physicians have difficulty accepting clinical decision support systems? Stud Health Technol Inform. 2013;192:1202.",{},{"id":18,"text":761,"url":18,"identifiers":762},"Sperl-Hillen JM, Rossom RC, Kharbanda EO, Gold R, Geissal ED, Elliott TE, et al. Priorities wizard: multisite web-based primary care clinical decision support improved chronic care outcomes with high use rates and high clinician satisfaction rates. EGEMS (Wash DC). 2019;7(1):9.",{},{"id":18,"text":764,"url":18,"identifiers":765},"Desai J, Saman D, Sperl-Hillen JM, Pratt R, Dehmer SP, Allen C, Ohnsorg K, Wuorio A, Appana D, Hitz P, Land A, Sharma R, Wilkinson L, Crain AL, Crabtree BF, Bianco J, O’Connor PJ. Implementing a prediabetes clinical decision support system in a large primary care system: design, methods, and pre-implementation results. Contemp Clin Trials. 2022;114:106686.",{"doi":766},"10.1016\u002Fj.cct.2022.106686",{"id":18,"text":768,"url":18,"identifiers":769},"Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208.",{"doi":770},"10.1016\u002Fj.jbi.2019.103208",{"id":18,"text":772,"url":18,"identifiers":773},"Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.",{"doi":774},"10.1016\u002Fj.jbi.2008.08.010",{"id":18,"text":776,"url":18,"identifiers":777},"Scholl I, Kriston L, Dirmaier J, et al. Development and psychometric properties of the shared decision making questionnaire: physician version (SDM-Q-Doc). Patient Educ Couns. 2012;88:284–90.",{"doi":778},"10.1016\u002Fj.pec.2012.03.005",{"id":18,"text":780,"url":18,"identifiers":781},"JB. SUS: a 'quick and dirty' usability scale. In: Jordan PW TB, Weerdmeester BA, McClelland AL, editor. Usability evaluation in industry. London: Taylor and Francis; 1996. p. 189–94.",{},{"id":18,"text":783,"url":18,"identifiers":784},"SAS Institute Inc. Version 9.4. Cary, North Carolina, USA. 2013.",{},{"id":18,"text":786,"url":18,"identifiers":787},"Harry ML, Saman DM, Truitt AR, Allen CI, Walton KM, O’Connor PJ, et al. Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system. BMC Med Inform Decis Mak. 2020;20(1):117.",{"doi":788},"10.1186\u002Fs12911-020-01136-8",{"id":18,"text":790,"url":18,"identifiers":791},"O’Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, Asche SE, et al. Impact of electronic health record clinical decision support on diabetes care: a randomized trial. Ann Fam Med. 2011;9(1):12–21.",{"doi":792},"10.1370\u002Fafm.1196",{"id":18,"text":794,"url":18,"identifiers":795},"Sperl-Hillen JM, Crain AL, Margolis KL, Ekstrom HL, Appana D, Amundson GH, et al. Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial. J Am Med Inform Assoc. 2018;25(9):1137–46.",{"doi":796},"10.1093\u002Fjamia\u002Focy085",{"id":18,"text":798,"url":18,"identifiers":799},"Salwei ME, Carayon P, Hoonakker PLT, Hundt AS, Wiegmann D, Pulia M, Patterson BW. Workflow integration analysis of a human factors-based clinical decision support in the emergency department. Appl Ergon. 2021;97:103498.",{"doi":800},"10.1016\u002Fj.apergo.2021.103498",{"id":18,"text":802,"url":18,"identifiers":803},"Harry ML, Truitt AR, Saman DM, Henzler-Buckingham HA, Allen CI, Walton KM, Ekstrom HL, O'Connor PJ, Sperl-Hillen JM, Bianco JA, Elliott TE. Barriers and facilitators to implementing cancer prevention clinical decision support in primary care: a qualitative study. BMC Health Serv Res. 2019;19(1):534.",{"doi":804},"10.1186\u002Fs12913-019-4326-4",{"id":18,"text":806,"url":18,"identifiers":807},"Pratt R, Saman DM, Allen C, Crabtree B, Ohnsorg K, Sperl-Hillen JM, Harry M, Henzler-Buckingham H, O'Connor PJ, Desai J. Assessing the implementation of a clinical decision support tool in primary care for diabetes prevention: a qualitative interview study using the Consolidated Framework for Implementation Science. BMC Med Inform Decis Mak. 2022;22(1):15.",{"doi":808},"10.1186\u002Fs12911-021-01745-x",{"id":18,"text":810,"url":18,"identifiers":811},"Gilmer TP, O’Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, et al. Cost effectiveness of an electronic medical record based clinical decision support system. Health Serv Res. 2012;47(6):2137–58.",{"doi":812},"10.1111\u002Fj.1475-6773.2012.01427.x",{"id":814,"createTime":815,"updateTime":816,"relativeEntities":817,"slug":818,"properties":819,"entityType":125,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":831,"translateLanguages":832,"viewCount":19,"primaryUrl":833,"fullTextUrl":18,"authors":834,"publicationType":220,"publisherRelationship":1148,"citationCount":96,"citationInfo":1198,"publishDate":18,"publishYear":18,"citationAnalyzeStatus":1200,"lastCitationAnalyze":1201,"indexDatabases":1202,"openAccess":18,"references":1203,"isForceReanalyzing":279},"3dbe4730-5711-4661-bf49-e9592ebc7186","2024-04-14T15:07:31.166+00:00","2026-09-05T04:13:17.574+00:00",[],"Developing-an-integrated-clinical-decision-support-system-for-the-early-identification-and-management-of-kidney-disease-building-cross-sectoral-partnerships",{"openalex":820,"abstract":822,"title":824,"pm":827,"doi":829},{"VOID":821},"W4392616660",{"EN":823},"\u003Cjats:title>Abstract\u003C\u002Fjats:title>\u003Cjats:sec>\n                \u003Cjats:title>Background\u003C\u002Fjats:title>\n                \u003Cjats:p>The burden of chronic conditions is growing in Australia with people in remote areas experiencing high rates of disease, especially kidney disease. Health care in remote areas of the Northern Territory (NT) is complicated by a mobile population, high staff turnover, poor communication between health services and complex comorbid health conditions requiring multidisciplinary care.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Aim\u003C\u002Fjats:title>\n                \u003Cjats:p>This paper aims to describe the collaborative process between research, government and non-government health services to develop an integrated clinical decision support system to improve patient care.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Methods\u003C\u002Fjats:title>\n                \u003Cjats:p>Building on established partnerships in the government and Aboriginal Community-Controlled Health Service (ACCHS) sectors, we developed a novel digital clinical decision support system for people at risk of developing kidney disease (due to hypertension, diabetes, cardiovascular disease) or with kidney disease. A cross-organisational and multidisciplinary Steering Committee has overseen the design, development and implementation stages. Further, the system’s design and functionality were strongly informed by experts (Clinical Reference Group and Technical Working Group), health service providers, and end-user feedback through a formative evaluation.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Results\u003C\u002Fjats:title>\n                \u003Cjats:p>We established data sharing agreements with 11 ACCHS to link patient level data with 56 government primary health services and six hospitals. Electronic Health Record (EHR) data, based on agreed criteria, is automatically and securely transferred from 15 existing EHR platforms. Through clinician-determined algorithms, the system assists clinicians to diagnose, monitor and provide guideline-based care for individuals, as well as service-level risk stratification and alerts for clinically significant events.\u003C\u002Fjats:p>\n              \u003C\u002Fjats:sec>\u003Cjats:sec>\n                \u003Cjats:title>Conclusion\u003C\u002Fjats:title>\n                \u003Cjats:p>Disconnected health services and separate EHRs result in information gaps and a health and safety risk, particularly for patients who access multiple health services. However, barriers to clinical data sharing between health services still exist. 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SAGE Open Med. 2018;6.",{"doi":1247},"10.1177\u002F2050312118769211",{"id":18,"text":1249,"url":18,"identifiers":1250},"Matthews V, Burgess C, Connors C, Moore E, Peiris D, Scrimgeour D, Thompson S, Larkins S, Bailie R. Integrated clinical decision support Systems promote Absolute Cardiovascular Risk Assessment: an important primary Prevention measure in Aboriginal and Torres Strait Islander Primary Health Care. Front Public Health. 2017;5.",{"doi":1251},"10.3389\u002Ffpubh.2017.00233",{"id":18,"text":1253,"url":18,"identifiers":1254},"Savage E, Hegarty J, Weathers E, Mulligan L, Bradley C, Condon C, et al. Transforming chronic illness management through Integrated Care: a systematic review of what works best and why. Int J Integr Care. 2016;16(6):A394.",{"doi":1255},"10.5334\u002Fijic.2942",{"id":18,"text":1257,"url":18,"identifiers":1258},"Nolan-Isles D, Macniven R, Hunter K, Gwynn J, Lincoln M, Moir R, Dimitropoulos Y, Taylor D, Agius T, Finlayson H, Martin R, Ward K, Tobin S, Gwynne K. Enablers and barriers to Accessing Healthcare Services for Aboriginal People in New South Wales, Australia. Int J Environ Res Public Health. 2021;18(6).",{"doi":1259},"10.3390\u002Fijerph18063014",{"id":18,"text":1261,"url":18,"identifiers":1262},"Australian Institute of Health Welfare. Cardiovascular disease, diabetes and chronic kidney disease: Australian facts: morbidity - hospital care. In: AIHW, editor. Cardiovascular, diabetes and chronic kidney disease series no3. Canberra: AIHW; 2014.",{},{"id":18,"text":1264,"url":18,"identifiers":1265},"Chen W, Howard K, Gorham G, O’Bryan CM, Coffey P, Balasubramanya B, Abeyaratne A, Cass A. Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis. J Am Med Inform Assoc. 2022:ocac110.",{"doi":1266},"10.1093\u002Fjamia\u002Focac110",{"id":18,"text":1268,"url":18,"identifiers":1269},"Chen W, O’Bryan CM, Gorham G, Howard K, Balasubramanya B, Coffey P, Abeyaratne A, Cass A. Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation. J Implement Sci Commun. 2022;3(1):1–20.",{"doi":1270},"10.1186\u002Fs43058-021-00234-6",{"id":18,"text":1272,"url":18,"identifiers":1273},"Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, Hinder S, Procter R, Shaw S. Analysing the role of complexity in explaining the fortunes of technology programmes: empirical application of the NASSS framework. BMC Med. 2018;16(1):66.",{"doi":1274},"10.1186\u002Fs12916-018-1050-6",{"id":18,"text":1276,"url":18,"identifiers":1277},"Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A’Court C, Hinder S, Fahy N, Procter R, Shaw S. Beyond adoption: a New Framework for Theorizing and evaluating nonadoption, abandonment, and challenges to the Scale-Up, Spread, and sustainability of Health and Care technologies. J Med Internet Res. 2017;19(11):e367.",{"doi":1278},"10.2196\u002Fjmir.8775",{"id":18,"text":1280,"url":18,"identifiers":1281},"Harry E, Pierce RG, Kneeland P, Huang G, Stein J, Sweller J. Cognitive load and its implications for health care. NEJM Catalyst. 2018;4(2).",{},{"id":18,"text":1283,"url":18,"identifiers":1284},"Snipp CM. What does data sovereignty imply: what does it look like? In: Tahu Kukutai and John Taylor, editor. Indigenous data sovereignty: Toward an agenda. Canberra: The Australian National University ANU Press, Australia; 2016. p. 39–56.",{"doi":1285},"10.22459\u002FCAEPR38.11.2016.03",{"id":18,"text":1287,"url":18,"identifiers":1288},"NHS Digital. Clinical Risk Management: Its Application in the Deployment and Use of Health IT Systems - Implementation Guide. In: Standardisation Committee for Care Information, editor. Surrey UK.2016.",{},{"id":18,"text":1290,"url":18,"identifiers":1291},"NHS Digital. Clinical Risk Management: Its Application in the Manufacture of Health IT Systems - Implementation Guide. In: Standardisation Committee for Care Information, editor. Surrey UK.2016.",{},{"id":18,"text":1293,"url":18,"identifiers":1294},"Chen W, Abeyaratne A, Gorham G, George P, Karepalli V, Tran D, Brock C, Cass A. Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia. BMC Nephrol. 2022;23(1):320.",{"doi":1295},"10.1186\u002Fs12882-022-02947-9",{"id":18,"text":1297,"url":18,"identifiers":1298},"Velickovski F, Ceccaroni L, Roca J, Burgos F, Galdiz JB, Marina N, Lluch-Ariet M. Clinical decision support systems (CDSS) for preventive management of COPD patients. J Translational Med. 2014;12:1–10.",{"doi":1299},"10.1186\u002F1479-5876-12-S2-S9",{"id":18,"text":1301,"url":18,"identifiers":1302},"KDIGO. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3:1–150.",{"doi":1303},"10.1038\u002Fkisup.2012.73",{"id":18,"text":1305,"url":18,"identifiers":1306},"Gabb GM, Mangoni AA, Anderson CS, Cowley D, Dowden JS, Golledge J, Hankey GJ, Howes FS, Leckie L, Perkovic V. Guideline for the diagnosis and management of hypertension in adults—2016. Med J Aust. 2016;205(2):85–9.",{"doi":1307},"10.5694\u002Fmja16.00526",{"id":18,"text":1309,"url":18,"identifiers":1310},"Gliklich RE. Managing Patient Identity Across Data Sources. In: Dreyer NA, Leavy MB, editors. Registries for Evaluating Patient Outcomes: A User’s Guide [Internet]. 3rd edition. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014.",{},{"id":18,"text":1312,"url":18,"identifiers":1313},"Khairat S, Coleman C, Ottmar P, Jayachander DI, Bice T, Carson SS. Association of Electronic Health Record Use with physician fatigue and efficiency. JAMA Netw Open. 2020;3(6):e207385–e.",{"doi":1314},"10.1001\u002Fjamanetworkopen.2020.7385",{"id":18,"text":1316,"url":18,"identifiers":1317},"Gulla J, Neri PM, Bates DW, Samal L. User requirements for a chronic kidney disease clinical decision Support Tool to Promote Timely Referral. Int J Med Informatics. 2017;101:50–7.",{"doi":1318},"10.1016\u002Fj.ijmedinf.2017.01.018",{"id":18,"text":1320,"url":18,"identifiers":1321},"Margheri A, Masi M, Miladi A, Sassone V, Rosenzweig J. Decentralised provenance for healthcare data. Int J Med Informatics. 2020;141:104197.",{"doi":1322},"10.1016\u002Fj.ijmedinf.2020.104197",{"id":1324,"createTime":1325,"updateTime":1326,"relativeEntities":1327,"slug":1328,"properties":1329,"entityType":125,"verifyStatus":126,"verifyTime":1338,"verifyNote":128,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1339,"fullTextUrl":18,"authors":1340,"publicationType":220,"publisherRelationship":1426,"citationCount":19,"citationInfo":1481,"publishDate":1484,"publishYear":1482,"citationAnalyzeStatus":1200,"lastCitationAnalyze":1326,"indexDatabases":1485,"openAccess":18,"references":1486,"isForceReanalyzing":279},"755c660c-f0cb-4a9f-8024-6116813f2c88","2023-12-13T16:01:21.049+00:00","2026-07-30T14:11:16.257+00:00",[],"A-Bayesian-spatio-temporal-approach-for-real-time-detection-of-disease-outbreaks-a-case-study",{"abstract":1330,"title":1332,"gsPaper":1334,"doi":1336},{"EN":1331},"For researchers and public health agencies, the complexity of high–dimensional spatio–temporal data in surveillance for large reporting networks presents numerous challenges, which include low signal–to–noise ratios, spatial and temporal dependencies, and the need to characterize uncertainties. Central to the problem in the context of disease outbreaks is a decision structure that requires trading off false positives for delayed detections. In this paper we apply a previously developed Bayesian hierarchical model to a data set from the Indiana Public Health Emergency Surveillance System (PHESS) containing three years of emergency department visits for influenza–like illness and respiratory illness. Among issues requiring attention were selection of the underlying network (Too few nodes attenuate important structure, while too many nodes impose barriers to both modeling and computation.); ensuring that confidentiality protections in the data do not impede important modeling day of week effects; and evaluating the performance of the model. Our results show that the model captures salient spatio–temporal dynamics that are present in public health surveillance data sets, and that it appears to detect both “annual” and “atypical” outbreaks in a timely, accurate manner. We present maps that help make model output accessible and comprehensible to public health authorities. We use an illustrative family of decision rules to show how output from the model can be used to inform false positive–delayed detection tradeoffs. The advantages of our methodology for addressing the complicated issues of real world surveillance data applications are three–fold. We can easily incorporate additional covariate information and spatio–temporal dynamics in the data. Second, we furnish a unified framework to provide uncertainties associated with each parameter. Third, we are able to handle multiplicity issues by using a Bayesian approach. The urgent need to quickly and effectively monitor the health of the public makes our methodology a potentially plausible and useful surveillance approach for health professionals.",{"EN":1333},"A Bayesian spatio–temporal approach for real–time detection of disease outbreaks: a case 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Stat Med. 1999, 18: 2111-2122. 10.1002\u002F(SICI)1097-0258(19990830)18:16\u003C2111::AID-SIM171>3.0.CO;2-Q.","https:\u002F\u002Fdoi.org\u002F10.1002\u002F(sici)1097-0258(19990830)18:16\u003C2111::aid-sim171>3.3.co;2-h",{"openalex":1503,"doi":1504},"W4241956284","10.1002\u002F(sici)1097-0258(19990830)18:16",{"id":18,"text":1506,"url":1507,"identifiers":1508},"Cowling BJ, Wong IOL, Ho L–M, Riley S, Leung GM:Methods for monitoring influenza surveillance data. Int J Epidemiol. 2006, 35: 1314-1321. 10.1093\u002Fije\u002Fdyl162.","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fije\u002Fdyl162",{"mag":1509,"openalex":1510,"pm":1511,"doi":1512},"2152614795","W2152614795","16926216","10.1093\u002Fije\u002Fdyl162",{"id":18,"text":1514,"url":1515,"identifiers":1516},"Fricker RD, Hegler BL, Dunfee DA:Comparing syndromic surveillance detection methods: EARS versus a CUSUM–based methology. 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Stat Med. 2012, 31: 2123-2136. 10.1002\u002Fsim.5350.","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.5350",{"mag":1542,"openalex":1543,"pm":1544,"doi":1545},"2077754282","W2077754282","22388709","10.1002\u002Fsim.5350",{"id":18,"text":1547,"url":1548,"identifiers":1549},"Knorr–Held L, Richardson S:A hierarchical model for space–time surveillance data on meningococcal disease incidence. J Roy Stat Soc C. 2003, 52: 169-183. 10.1111\u002F1467-9876.00396.","https:\u002F\u002Fdoi.org\u002F10.1111\u002F1467-9876.00396",{"mag":1550,"openalex":1551,"doi":1552},"2118754020","W2118754020","10.1111\u002F1467-9876.00396",{"id":18,"text":1554,"url":1555,"identifiers":1556},"Martínez–Beneito MA, Conesa D, López–Quílez A, López–Maside A:Bayesian Markov switching models for the early detection of influenza epidemics. Stat Med. 2008, 27: 4455-4468. 10.1002\u002Fsim.3320.","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.3320",{"mag":1557,"openalex":1558,"pm":1559,"doi":1560},"2039307858","W2039307858","18618414","10.1002\u002Fsim.3320",{"id":18,"text":1562,"url":1563,"identifiers":1564},"Zhou H, Lawson AB:EWMA smoothing and Bayesian spatial modeling for health surveillance. Stat Med. 2008, 27: 5907-5928. 10.1002\u002Fsim.3409.","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fsim.3409",{"mag":1565,"openalex":1566,"pm":1567,"doi":1568},"2087913418","W2087913418","18759375","10.1002\u002Fsim.3409",{"id":1494,"text":1570,"url":1496,"identifiers":1571},"Keeling MJ, Rohani P: Modeling Infectious Diseases In Humans And Animals . 2007, Princeton University Press, Princeton",{"doi":1498},{"id":18,"text":1573,"url":1574,"identifiers":1575},"Tokars JI, Burkom H, Xing J, English R, Bloom S, Cox K, Pavlin JA:Enhancing time series detection algorithms for automated biosurveillance. Emerg Infect Dis. 2009, 15: 533-539. 10.3201\u002F1504.080616.","https:\u002F\u002Fdoi.org\u002F10.3201\u002Feid1504.080616",{"mag":1576,"pmc":1577,"openalex":1578,"pm":1579,"doi":1580},"2146226171","2671446","W2146226171","19331728","10.3201\u002Feid1504.080616",{"id":1582,"text":1583,"url":1584,"identifiers":1585},"82ef5b9a-434a-4f59-b26c-cf2054925a48","Hafen RP, Anderson DE, Cleveland WS, Maciejewski R, Ebert DS, Abusalah A, Yakout M, Ouzzani M, Grannis SJ:Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts. BMC Med Inform Decis Mak. 2009, 9: 21-10.1186\u002F1472-6947-9-21. doi:10.1186\u002F1472–6947–9–21,","https:\u002F\u002Fbmcmedinformdecismak.biomedcentral.com\u002Farticles\u002F10.1186\u002F1472-6947-9-21",{"doi":1586},"10.1186\u002F1472-6947-9-21",{"id":18,"text":1588,"url":18,"identifiers":1589},"Box GEP, Jenkins GM, Reinsel GC: Time Series Analysis: Forecasting and Control . 2008, Wiley, New Jersey",{},{"id":1494,"text":1591,"url":1496,"identifiers":1592},"Grannis SJ, Biondich PG, Mamlin BW, Wilson G, Jones L, Overhage JM: How Disease Surveillance Systems Can Serve as Practical Building Blocks for a Health Information Infrastructure: the Indiana Experience. Am Med Inf Assoc Annu Symp Proc 2005:286–290.",{"doi":1498},{"id":18,"text":1594,"url":18,"identifiers":1595},"Grannis SJ, Wade M, Gibson J, Overhage JM: The Indiana Public Health Emergency Surveillance System: ongoing progress, early findings, and future direction6. Am Med Inf Assoc Annu Symp Proc 2005:304–308.",{},{"id":1597,"text":1598,"url":1599,"identifiers":1600},"f96759d3-e8eb-4bf3-9883-37e51fa0c3f2","Chapman WW, Dowling JN, Wagner MM:Classification of emergency department chief complaints into 7 syndromes: a retrospective analysis of 527,228 patients. Ann Emerg Med. 2005, 46: 445-455. 10.1016\u002Fj.annemergmed.2005.04.012.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0196064405004646",{"doi":1601},"10.1016\u002Fj.annemergmed.2005.04.012",{"id":18,"text":1603,"url":1604,"identifiers":1605},"Scott JG, Berger JO:An exploration of aspects of Bayesian multiple testing. J Stat Plann Infer. 2006, 136: 2144-2162. 10.1016\u002Fj.jspi.2005.08.031.","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jspi.2005.08.031",{"mag":1606,"openalex":1607,"doi":1608},"2031913033","W2031913033","10.1016\u002Fj.jspi.2005.08.031",{"id":1610,"createTime":1611,"updateTime":1612,"relativeEntities":1613,"slug":1614,"properties":1615,"entityType":125,"verifyStatus":126,"verifyTime":1626,"verifyNote":128,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1627,"fullTextUrl":18,"authors":1628,"publicationType":220,"publisherRelationship":1661,"citationCount":19,"citationInfo":1715,"publishDate":1718,"publishYear":1716,"citationAnalyzeStatus":1719,"lastCitationAnalyze":1720,"indexDatabases":1721,"openAccess":18,"references":18,"isForceReanalyzing":279},"86b94fe8-d20f-4e5f-8106-66b61ba1e030","2024-02-13T04:57:17.581+00:00","2026-07-29T13:44:13.340+00:00",[],"Clinical-decision-support-system-for-quality-of-life-among-the-elderly-an-approach-using-artificial-neural-network",{"abstract":1616,"title":1618,"gsPaper":1620,"references":1622,"doi":1624},{"EN":1617},"Due to advancements in medicine and the elderly population’s growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social and environmental participation among them by considering all factors affecting it. So far, it is not designed the prediction system for QoL in this regard. Therefore, this study aimed to develop the CDSS based on ANN as an ML technique by considering the physical, psychiatric, and social factors. In this developmental and applied study, we investigated the 980 cases associated with pleasant and unpleasant elderlies QoL cases. We used the BLR and simple correlation coefficient methods to attain the essential factors affecting the QoL among the elderly. Then three BP configurations, including CF-BP, FF-BP, and E-BP, were compared to get the best model for predicting the QoL. Based on the BLR, the 13 factors were considered the best factors affecting the elderly’s QoL at P \u003C 0.05. Comparing all ANN configurations showed that the CF-BP with the 13-16-1 structure with sensitivity = 0.95, specificity  =  0.97, accuracy = 0.96, F-Score = 0.96, PPV = 0.95, and NPV = 0.97 gained the best performance for QoL among the elderly. The results of this study showed that the designed CDSS based on the CFBP could be considered an efficient tool for increasing the QoL among the elderly.",{"EN":1619},"Clinical decision support system for quality of life among the elderly: an approach using artificial neural network",{"VOID":1621},"[\"2128420473745157613\"]",{"VOID":1623},"Nurov N. indication for morphometric parameters of the craniofacial region of elderly people with partial and complete adhesion. World Bull Public Health. 2022;8:91–3.\nSmith RJ, Lehning AJ, Kim K. Aging in place in gentrifying neighborhoods: implications for physical and mental health. Gerontologist. 2018;58(1):26–35.\nNichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. 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J Educ Health Promot. 2021;10:449.\nSun W, Aodeng S, Tanimoto Y, Watanabe M, Han J, Wang B, et al. quality of life (QOL) of the community-dwelling elderly and associated factors: a population-based study in urban areas of China. Arch Gerontol Geriatr. 2015;60(2):311–6.\nSuardana W, Yusuf A, Purnomo W. Self-help group therapy: the enhancement of self-care ability and quality of life among the elderly in Bali, Indonesia. Indian J Public Health Res Dev. 2018;9(11):76–80.\nHeidari M, Sheikhi RA, Rezaei P, Abyaneh SK. Comparing quality of life of elderly menopause living in urban and rural areas. J Menopaus Med. 2019;25(1):28–34.\nSuksawatchon U, Suksawatchon J, Lawang W. Health risk analysis expert system for family caregiver of person with disabilities using data mining techniques. ECTI Trans Comput Inf Technol. 2018;12(1):62–72.\nZhang Y, Guo S-L, Han L-N, Li T-L. Application and exploration of big data mining in clinical medicine. Chin Med J. 2016;129(06):731–8.\nPanicker SS, Gayathri P. A survey of machine learning techniques in physiology based mental stress detection systems. Biocybern Biomed Eng. 2019;39(2):444–69.\nSau A, Bhakta I. Predicting anxiety and depression in elderly patients using machine learning technology. Healthc Technol Lett. 2017;4(6):238–43.\nGrządzielewska M. Using machine learning in burnout prediction: a survey. Child Dolesc Soc Work J. 2021;38(2):175–80.\nByeon H. Exploring factors for predicting anxiety disorders of the elderly living alone in South Korea using interpretable machine learning: a population-based study. Int J Environ Res Public Health. 2021;18(14):7625.\nYacchirema D, de Puga JS, Palau C, Esteve M. Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Pers Ubiquit Comput. 2019;23(5):801–17.\nPrerana PS, Taneja K. Predictive data mining for diagnosis of thyroid disease using neural network. Int J Res Manag Sci Technol. 2015;3(2):75–80.\nJahani A, Saffariha M. Human activities impact prediction in vegetation diversity of Lar National Park in Iran using artificial neural network model. Integr Environ Assess Manag. 2021;17(1):42–52.\nShanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Design of an artificial neural network to predict mortality among COVID-19 patients. Inform Med Unlock. 2022;31:100983.\nJahani A, Saffariha M. Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques. Sci Rep. 2021;11(1):1124.\nShahmoradi L, Liraki Z, Karami M, Savareh BA, Nosratabadi M. Development of decision support system to predict neurofeedback response in ADHD: an artificial neural network approach. Acta Inform Med: AIM: J Soc Med Inform Bosnia Herzeg: Casopis Drustva za Med Inform BiH. 2019;27(3):186–91.\nShanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Developing an artificial neural network for detecting COVID-19 disease. 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Automatic disability categorisation based on ADLs among older adults in a nationally representative population using data mining methods. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2019, pp. 23–27.\nLee S-H, Choi I, Ahn W-Y, Shin E, Cho S-I, Kim S, et al. Estimating quality of life with biomarkers among older Korean adults: a machine-learning approach. Arch Gerontol Geriatr. 2020;87:103966.\nNa K-S. Prediction of future cognitive impairment among the community elderly: a machine-learning based approach. Sci Rep. 2019;9(1):1–9.\nByeon H. Developing a model to predict the social activity participation of the senior citizens living in South Korea by combining artificial neural network and quest algorithm. Int J Eng Technol. 2019;8(1.4):214–21.",{"VOID":1625},"10.1186\u002Fs12911-022-02044-9","2024-05-16T12:00:18.830+00:00","https:\u002F\u002Fbmcmedinformdecismak.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12911-022-02044-9",[1629,1646],{"id":1630,"sortIndex":19,"researcher":18,"roles":1631,"affiliations":1632,"properties":1641,"displayName":1643,"givenName":18,"familyName":18},"e657cc1e-a6c9-4221-afe0-a0ec04879f5f",[136],[1633],{"id":1634,"sortIndex":19,"affiliation":1635,"properties":18},"c182ab9b-1dd5-492d-80ac-bdf09a9b976a",{"id":1634,"createTime":18,"updateTime":18,"relativeEntities":1636,"slug":18,"properties":1637,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1640,"statistic":18},[],{"title":1638},{"VI":1639},"Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran",[],{"title":1642,"gsAuthor":1644},{"VI":1643},"Maryam Ahmadi",{"VOID":1645},"[\"Dwqba-4AAAAJ\"]",{"id":1647,"sortIndex":96,"researcher":18,"roles":1648,"affiliations":1649,"properties":1656,"displayName":1658,"givenName":18,"familyName":18},"a2cee7fe-eff3-4cb8-b5cf-0125cf7ca71b",[136],[1650],{"id":1634,"sortIndex":19,"affiliation":1651,"properties":18},{"id":1634,"createTime":18,"updateTime":18,"relativeEntities":1652,"slug":18,"properties":1653,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1655,"statistic":18},[],{"title":1654},{"VI":1639},[],{"title":1657,"gsAuthor":1659},{"VI":1658},"Raoof Nopour",{"VOID":1660},"[\"KvGIadEAAAAJ\"]",{"url":1627,"publisher":1662,"properties":1711},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1663,"slug":10,"properties":1664,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1667,"manageAffiliations":1680,"indexDatabases":1691,"url":91,"thumbnailPath":18,"statistic":1706,"gsStatistic":18,"type":104,"analyzePriority":18},[],{"issn":1665,"title":1666},{"VOID":13},{"EN":15},[1668,1672,1676],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":1669,"label":1670,"description":1671,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":1673,"label":1674,"description":1675,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},{"id":34,"createTime":18,"updateTime":18,"relativeEntities":1677,"label":1678,"description":1679,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":37},{},[1681,1686],{"id":41,"createTime":18,"updateTime":18,"relativeEntities":1682,"slug":18,"properties":1683,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1685,"statistic":18},[],{"title":1684},{"EN":45},[],{"id":48,"createTime":18,"updateTime":18,"relativeEntities":1687,"slug":18,"properties":1688,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1690,"statistic":18},[],{"title":1689},{"EN":52},[],[1692,1699],{"id":56,"indexDatabase":1693,"url":67,"indexYears":68,"academicFieldIds":1698,"indexDatabaseRanking":73},{"id":58,"createTime":18,"updateTime":18,"relativeEntities":1694,"label":1695,"description":1696,"key":64,"publicationTags":1697,"standard":18},[],{"EN":61,"VI":61},{"EN":61,"VI":63},[66],[70,71,72],{"id":75,"indexDatabase":1700,"url":88,"indexYears":18,"academicFieldIds":1705,"indexDatabaseRanking":18},{"id":77,"createTime":18,"updateTime":18,"relativeEntities":1701,"label":1702,"description":1703,"key":84,"publicationTags":1704,"standard":18},[],{"EN":80,"VI":80},{"EN":82,"VI":83},[86,87],[90],{"impactFactor":19,"impactFactorByYear":1707,"i10Index":96,"i10IndexLast5Year":19,"totalPublication":97,"totalPublicationByYear":1708,"totalCitation":100,"totalCitationByYear":1709,"totalCitationPerPublication":102,"totalCitationPerPublicationByYear":1710,"hindexLast5Year":96,"hindex":96},{"2010":94,"2011":95},{"2007":96,"2009":96,"2011":96,"2012":96,"2013":94,"2014":94,"2016":94,"2017":94,"2019":95,"2020":95,"2021":99,"2022":95,"2024":94},{"2009":100},{"2009":100},{"pages":1712,"volume":1713},{"VOID":423},{"VOID":1714},"22",{"total":19,"publishYear":1716,"statisticByYear":1717},2022,{},"2022-11-12","DONE_ANALYZE_CITATION","2026-07-29T13:44:13.339+00:00",[86,73],{"id":1723,"createTime":1724,"updateTime":1725,"relativeEntities":1726,"slug":1727,"properties":1728,"entityType":125,"verifyStatus":126,"verifyTime":1737,"verifyNote":128,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1738,"fullTextUrl":18,"authors":1739,"publicationType":220,"publisherRelationship":1824,"citationCount":1879,"citationInfo":1880,"publishDate":1883,"publishYear":1881,"citationAnalyzeStatus":1719,"lastCitationAnalyze":1725,"indexDatabases":1884,"openAccess":18,"references":1885,"isForceReanalyzing":279},"cc6ccf6c-affb-476f-800b-71fead8e2e98","2024-01-21T12:17:18.966+00:00","2026-07-29T06:01:42.134+00:00",[],"Physicians-pharmacogenomics-information-needs-and-seeking-behavior-a-study-with-case-vignettes",{"abstract":1729,"title":1731,"gsPaper":1733,"doi":1735},{"EN":1730},"Genetic testing, especially in pharmacogenomics, can have a major impact on patient care. However, most physicians do not feel that they have sufficient knowledge to apply pharmacogenomics to patient care. Online information resources can help address this gap. We investigated physicians’ pharmacogenomics information needs and information-seeking behavior, in order to guide the design of pharmacogenomics information resources that effectively meet clinical information needs. We performed a formative, mixed-method assessment of physicians’ information-seeking process in three pharmacogenomics case vignettes. Interactions of 6 physicians’ with online pharmacogenomics resources were recorded, transcribed, and analyzed for prominent themes. Quantitative data included information-seeking duration, page navigations, and number of searches entered. We found that participants searched an average of 8 min per case vignette, spent less than 30 s reviewing specific content, and rarely refined search terms. Participants’ information needs included a need for clinically meaningful descriptions of test interpretations, a molecular basis for the clinical effect of drug variation, information on the logistics of carrying out a genetic test (including questions related to cost, availability, test turn-around time, insurance coverage, and accessibility of expert support).Also, participants sought alternative therapies that would not require genetic testing. This study of pharmacogenomics information-seeking behavior indicates that content to support their information needs is dispersed and hard to find. Our results reveal a set of themes that information resources can use to help physicians find and apply pharmacogenomics information to the care of their patients.",{"EN":1732},"Physicians’ pharmacogenomics information needs and seeking behavior: a study with case vignettes",{"VOID":1734},"[\"3064740414906640590\"]",{"VOID":1736},"10.1186\u002Fs12911-017-0510-9","2024-04-29T13:05:58.655+00:00","https:\u002F\u002Fbmcmedinformdecismak.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12911-017-0510-9",[1740,1764,1777,1794,1811],{"id":1741,"sortIndex":19,"researcher":18,"roles":1742,"affiliations":1743,"properties":1761,"displayName":1763,"givenName":18,"familyName":18},"71dc76c6-8860-448f-9cb4-f776faf5ca8b",[136],[1744,1752],{"id":1745,"sortIndex":19,"affiliation":1746,"properties":18},"4551ab48-8e7b-4f69-a4ec-9ca8a27d2f2d",{"id":1745,"createTime":18,"updateTime":18,"relativeEntities":1747,"slug":18,"properties":1748,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1751,"statistic":18},[],{"title":1749},{"VI":1750},"Department of Biomedical Informatics, University of Utah, Salt Lake City, USA",[],{"id":1753,"sortIndex":96,"affiliation":1754,"properties":1760},"d0ff2cab-1717-4581-b71a-27c3f6c410c4",{"id":1753,"createTime":18,"updateTime":18,"relativeEntities":1755,"slug":18,"properties":1756,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1759,"statistic":18},[],{"title":1757},{"VI":1758},"Intermountain Healthcare, West Valley, USA",[],{},{"title":1762},{"VI":1763},"Bret S. E. 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Formative evaluation of a patient-specific clinical knowledge summarization tool. Int J Med Inform. 2016;86:126–34.",{"doi":1498},{"id":2022,"text":2023,"url":2024,"identifiers":2025},"a20897c8-cba1-411d-8411-532b6ddbdf19","Berg BL. Qualitative research methods for the social sciences. 4th ed. Boston: Allyn and Bacon; 2001. p. 304.","https:\u002F\u002Fwww.goodreads.com\u002Fbook\u002Fshow\u002F3183443-qualitative-research-methods-for-the-social-sciences",{"isbn":2026,"isbn13":2027},"0205628079","9780205628070",{"id":1494,"text":2029,"url":1496,"identifiers":2030},"Bates MJ. The Design of Browsing and Berrypicking Techniques for the online search Interface. Online Rev. 1989;13(5):407–24.",{"doi":1498},{"id":18,"text":2032,"url":18,"identifiers":2033},"Del Fiol G, Workman TE, Gorman PN. Clinical questions raised by clinicians at the point of care: a systematic review. JAMA Intern Med. 2014;174(5):710–8.",{},{"id":1494,"text":2035,"url":1496,"identifiers":2036},"Johansen Taber KA, Dickinson BD. Pharmacogenomic knowledge gaps and educational resource needs among physicians in selected specialties. Pharmacogenomics Pers Med. 2014;7:145–62.",{"doi":1498},{"id":1494,"text":2038,"url":1496,"identifiers":2039},"Hollnagel E, Woods DD. Joint cognitive systems: foundations of cognitive systems engineering. Boca Raton: CRC Press; 2005. p. 236.",{"doi":1498},{"id":1494,"text":2041,"url":1496,"identifiers":2042},"Meats E, Brassey J, Heneghan C, Glasziou P. Using the turning research into practice (TRIP) database: how do clinicians really search? J Med Libr Assoc. 2007;95(2):156.",{"doi":1498},{"id":1494,"text":2044,"url":1496,"identifiers":2045},"González-González AI, Dawes M, Sánchez-Mateos J, Riesgo-Fuertes R, Escortell-Mayor E, Sanz-Cuesta T, Hernández-Fernández T. Information needs and information-seeking behavior of primary care physicians. Ann Fam Med. 2007;5(4):345–52.",{"doi":1498},{"id":1494,"text":2047,"url":1496,"identifiers":2048},"O’Carroll AM, Westby EP, Dooley J, Gordon KE. Information-seeking behaviors of medical students: a cross-sectional web-based survey. JMIR Med Educ. 2015;1(1):e4.",{"doi":1498},{"id":18,"text":2050,"url":18,"identifiers":2051},"Ingwersen P. Cognitive perspectives of information retrieval interaction: elements of a cognitive IR theory. J Doc. 1996;52(1):3–50.",{},{"id":2053,"text":2054,"url":2055,"identifiers":2056},"1cf13890-d228-4118-977a-3784914e22d9","Schardt C, Adams MB, Owens T, Keitz S, Fontelo P. Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inform Decis Mak. 2007;7:16.","https:\u002F\u002Fbmcmedinformdecismak.biomedcentral.com\u002Farticles\u002F10.1186\u002F1472-6947-7-16",{"doi":2057},"10.1186\u002F1472-6947-7-16",{"id":1494,"text":2059,"url":1496,"identifiers":2060},"Pirolli P, Card S. Information foraging. Psychol Rev. 1999;106(4):643–75.",{"doi":1498},{"id":2062,"createTime":2063,"updateTime":2064,"relativeEntities":2065,"slug":2066,"properties":2067,"entityType":125,"verifyStatus":126,"verifyTime":2080,"verifyNote":128,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":2081,"fullTextUrl":18,"authors":2082,"publicationType":220,"publisherRelationship":2202,"citationCount":19,"citationInfo":2252,"publishDate":2254,"publishYear":1881,"citationAnalyzeStatus":1719,"lastCitationAnalyze":2064,"indexDatabases":2255,"openAccess":18,"references":18,"isForceReanalyzing":279},"aa11938d-9948-49a4-813a-6413c8f69867","2024-04-05T16:05:32.081+00:00","2026-07-29T02:24:58.489+00:00",[],"Enriching-the-international-clinical-nomenclature-with-Chinese-daily-used-synonyms-and-concept-recognition-in-physician-notes",{"abstract":2068,"title":2070,"gsPaper":2072,"keywords":2074,"references":2076,"doi":2078},{"EN":2069},"It has been shown that the entities in everyday clinical text are often expressed in a way that varies from how they are expressed in the nomenclature. Owing to lots of synonyms, abbreviations, medical jargons or even misspellings in the daily used physician notes in clinical information system (CIS), the terminology without enough synonyms may not be adequately suitable for the task of Chinese clinical term recognition. This paper demonstrates a validated system to retrieve the Chinese term of clinical finding (CTCF) from CIS and map them to the corresponding concepts of international clinical nomenclature, such as SNOMED CT. The system focuses on the SNOMED CT with Chinese synonyms enrichment (SCCSE). The literal similarity and the diagnosis-related similarity metrics were used for concept mapping. Two CTCF recognition methods, the rule- and terminology-based approach (RTBA) and the conditional random field machine learner (CRF), were adopted to identify the concepts in physician notes. The system was validated against the history of present illness annotated by clinical experts. The RTBA and CRF could be combined to predict new CTCFs besides SCCSE persistently. Around 59,000 CTCF candidates were accepted as valid and 39,000 of them occurred at least once in the history of present illness. 3,729 of them were accordant with the description in referenced Chinese clinical nomenclature, which could cross map to other international nomenclature such as SNOMED CT. With the hybrid similarity metrics, another 7,454 valid CTCFs (synonyms) were succeeded in concept mapping. For CTCF recognition in physician notes, a series of experiments were performed to find out the best CRF feature set, which gained an F-score of 0.887. The RTBA achieved a better F-score of 0.919 by the CTCF dictionary created in this research. This research demonstrated that it is feasible to help the SNOMED CT with Chinese synonyms enrichment based on physician notes in CIS. With continuous maintenance of SCCSE, the CTCFs could be precisely retrieved from free text, and the CTCFs arranged in semantic hierarchy of SNOMED CT could greatly improve the meaningful use of electronic health record in China. The methodology is also useful for clinical synonyms enrichment in other languages.",{"EN":2071},"Enriching the international clinical nomenclature with Chinese daily used synonyms and concept recognition in physician notes",{"VOID":2073},"[\"1932469301152336497\"]",{"EN":2075},"",{"VOID":2077},"Turchin A, Kolatkar NS, Grant RW, et al. Using regular expressions to abstract blood pressure and treatment intensification information from the text of physician notes. J Am Med Inform Assoc. 2006;13(6):691–5. doi:10.1197\u002Fjamia.M2078.\nRamakrishnan N, Hanauer D, Keller B. Mining electronic health record. Computer. 2010;43(10):77–81. doi: 10.1109\u002FMC.2010.292.\nInternational Health Terminology Standards Development Organisation. SNOMED clinical terms user guide. 2012.\nSkeppstedt M, Kvist M, Dalianis H. Rule-based entity recognition and coverage of SNOMED CT in Swedish clinical text. Eur Lang Resour Assoc. 2012;1(3):1250–7.\nCornet R, Keizer ND. Forty years of SNOMED: a literature review. BMC Med Inform Decis Mak. 2008;8(Suppl 1(24)):1–6.\nBatool R, Khattak AM, Kim TS, et al. Automatic extraction and mapping of discharge summary’s concepts into SNOMED CT. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:4195–8. doi: 10.1109\u002FEMBC.2013.6610470.\nHeinze DT, Morsch ML, Holbrook J. Mining free-text medical records. J Am Med Inform Assoc. 2001;8(1):254–8.\nClaveau V. Translation of biomedical terms by inferring rewriting rules. Information retrieval in biomedicine: natural language processing for knowledge integration, IGI - global. 2009. Chap 6.\nYuwen S, Yang X. Research on the clinical terminology construction based on SNOMED. Seventh International Conference on Fuzzy Systems and Knowledge Discovery. IEEE. 2010;5:2224–8. doi: 10.1109\u002FFSKD.2010.5569538.\nZhu Y, Pan H, Zhou L, et al. Translation and localization of SNOMED CT in China: a pilot using SNOMED CT. Artif Intell Med. 2012;54(2):147–9. doi:10.1016\u002Fj.artmed.2011.12.002.\nMerabti T, Soualmia LF, Grosjean J, et al. Assisting the Translation of SNOMED CT into French. Stud Health Technol Inform. 2013;192:47–51. doi:10.3233\u002F978-1-61499-289-9-47.\nKim TY, Hardiker N, Coenen A. Inter-terminology mapping of nursing problems. J Biomed Inform. 2014;49(6):213–20.\nDeléger L, Merkel M, Zweigenbaum P. Translating medical terminologies through word alignment in parallel text corpora. J Biomed Inform. 2009;42(4):692–701. doi:10.1016\u002Fj.jbi.2009.03.002.\nPerez-De-Viñaspre O, Oronoz M. SNOMED CT in a language isolate: an algorithm for a semiautomatic translation. BMC Med Inform Decis Mak. 2015;15 Suppl 2:1–14.\nSkeppelstedt M, Dalianis H. Using SNOMED CT for high precision entity recognition in Swedish clinical text. In: 23rd international conference of the European federation for medical informatics. 2011.\nCruanes J, Romáferri MT, Lloret E. Measuring lexical similarity methods for textual mapping in nursing diagnoses in Spanish and SNOMED-CT. Stud Health Technol Inform. 2012;180(1):255–9. doi:10.3233\u002F978-1-61499-101-4-255.\nMiñarrogiménez JA, Hellrich J, Schulz S. Acquisition of character translation rules for supporting SNOMED CT localizations. Stud Health Technol Inform. 2015;210:597–601. doi:10.3233\u002F978-1-61499-512-8-597.\nDanya L, Tiejun H, Junlian L, et al. Construction and application of the Chinese unified medical language system. J Intelligence. 2011;30(2):147–51.\nZhou X, Wu Z, Yin A, et al. Ontology development for unified traditional Chinese medical language system. Artif Intell Med. 2004;32(1):15–27. doi:10.1016\u002Fj.artmed.2004.01.014.\nDemner-Fushman D, Chapman WW, McDonald CJ. What can natural language processing do for clinical decision support? J Biomed Inform. 2009;42(5):760–72. doi:10.1016\u002Fj.jbi.2009.08.007.\nUzuner O, South BR, Shen S, et al. 2010 i2b2VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc. 2011;18(5):552–6. doi:10.1136\u002Famiajnl-2011-000203.\nLv X, Guan Y, Deng B. Transfer learning based clinical concept extraction on data from multiple sources. J Biomed Inform. 2014;52:55–64. doi:10.1016\u002Fj.jbi.2014.05.006.\nSavova GK, Masanz JJ, Ogren PV, et al. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010;17(5):507–13. doi:10.1136\u002Fjamia.2009.001560.\nKipper-Schuler K, Kaggal V, Masanz J, et al. System evaluation on a named entity corpus from clinical notes. In: Language resources and evaluation conference, LREC. 2008. p. 3001–7.\nWang Y, Yu Z, Li C, et al. Supervised methods for symptom name recognition in free-text clinical records of traditional Chinese medicine: an empirical study. J Biomed Inform. 2014;47(2):91–104.\nLiu K, Hogan WR, Crowley RS. Natural language processing methods and systems for biomedical ontology learning. J Biomed Inform. 2011;44(1):163–79. doi:10.1016\u002Fj.jbi.2010.07.006.\nSalton G, Buckley C. Term-weighting approaches in automatic text retrieval. Inf Process Manage. 1988;24(5):513–23. doi:10.1016\u002F0306-4573(88)90021-0.\nZhang H, Yu H, Xiong D, et al. HHMM-based Chinese lexical analyzer ICTCLAS. In: Proceedings of the 2nd SIGHAN workshop on Chinese language processing. 2003. p. 184–7. doi:10.3115\u002F1119250.1119280.\nCRF++: Yet another CRF toolkit. https:\u002F\u002Ftaku910.github.io\u002Fcrfpp\u002F. Accessed on 1 May 2017.\nNie J, Gao J, Zhang J, et al. On the Use of Words and N-grams for Chinese Information Retrieval. Fifth International Workshop on Information Retrieval with Asian Languages. Hong Kong. 2000:141-148. doi: 10.1145\u002F355214.355235.\nNavigli R, Velardi P. Structural semantic interconnections: a knowledge-based approach to word sense disambiguatio. IEEE Trans Pattern Anal Mach Intell. 2005;27(7):1075–86. doi:10.1109\u002FTPAMI.2005.149.\nSogueroruiz C, Hindberg K, Rojoalvarez JL, et al. Support vector feature selection for early detection of anastomosis leakage from Bag-of-words in electronic health records. IEEE J Biomed Health Informatics. 2014;20(5):1404–15. doi:10.1109\u002FJBHI.2014.2361688.\nSkeppstedt M, Kvist M, Nilsson GH, et al. Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: an annotation and machine learning study. J Biomed Inform. 2014;49(5):148–58. doi:10.1016\u002Fj.jbi.2014.01.012.\nMortensen JM, Musen MA, Noy NF. Crowdsourcing the verification of relationships in biomedical ontologies. AMIA Annu Symp Proc. 2013;2013:1020–9.\nKontonatsios G, Mihăilă C, Korkontzelos I, et al. A Hybrid Approach to Compiling Bilingual Dictionaries of Medical Terms from Parallel Corpora. 2014;8791(2425):57–69. SLSP 2014, At Grenoble, France, Volume: Statistical Language and Speech Processing, Second International Conference. doi: 10.1007\u002F978-3-319-11397-5_4.\nNyström M, Merkel M, Ahrenberg L, et al. Creating a medical English-Swedish dictionary using interactive word alignment. BMC Med Inform Decis Mak. 2006;6(1):35. doi:10.1186\u002F1472-6947-6-35.\nHenriksson A, Skeppstedt M, Kvist M, et al. Corpus-driven terminology development: populating Swedish SNOMED CT with synonyms extracted from electronic health records. In: The workshop on biomedical natural language processing. 2013. p. 36–44.\nHenriksson A, Conway M, Duneld M, et al. Identifying synonymy between SNOMED clinical terms of varying length using distributional analysis of electronic health records. AMIA Proc AMIA Ann Symp AMIA Symp. 2013;2013:600–9.\nGrabar N, Varoutas PC, Rizand P, et al. Automatic acquisition of synonym resources and assessment of their impact on the enhanced search in EHRs. Methods Inf Med. 2009;48(2):149–54. doi:10.3414\u002FME9213.\nSchlegel DR, Crowner C, Elkin PL. Automatically expanding the synonym set of SNOMED CT using wikipedia. Stud Health Technol Inform. 2015;216:619–23.\nNing W, Yu M, Zhang R. A hierarchical method to automatically encode Chinese diagnoses through semantic similarity estimation. BMC Med Inform Decis Mak. 2016;16(1):1–12. doi:10.1186\u002Fs12911-016-0269-4.\nWang Y, Patrick J. Cascading classifiers for named entity recognition in clinical notes. In: The Workshop on Biomedical Information Extraction. Borovets, Bulgaria; 2009. p. 42–49.\nChapman WW, Dowling JN, Hripcsak G. Evaluation of training with an annotation schema for manual annotation of clinical conditions from emergency department reports. Int J Med Inform. 2008;77(2):107–13. doi:10.1016\u002Fj.ijmedinf.2007.01.002.\nGurulingappa H, Hofmann-Apitius M, Fluck J. Concept identification and assertion classification in patient health records. In: Proceedings of the 2010 i2b2\u002FVA Workshop on Challenges in Natural Language Processing for Clinical Data. Washington DC, USA; 2010.\nAgrawal A, He Z, Perl Y, et al. The readiness of SNOMED problem list concepts for meaningful use of electronic health records. Artif Intell Med. 2013;58(2):73–80. doi:10.1016\u002Fj.artmed.2013.03.008.",{"VOID":2079},"10.1186\u002Fs12911-017-0455-z","2024-08-31T01:27:00.754+00:00","https:\u002F\u002Fbmcmedinformdecismak.biomedcentral.com\u002Farticles\u002F10.1186\u002Fs12911-017-0455-z",[2083,2098,2127,2140,2153,2166,2181],{"id":2084,"sortIndex":19,"researcher":18,"roles":2085,"affiliations":2086,"properties":2095,"displayName":2097,"givenName":18,"familyName":18},"abcab08b-0df6-41aa-afa6-41629e01025f",[136],[2087],{"id":2088,"sortIndex":19,"affiliation":2089,"properties":18},"9f67b716-adbc-481f-a304-93555b603835",{"id":2088,"createTime":18,"updateTime":18,"relativeEntities":2090,"slug":18,"properties":2091,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2094,"statistic":18},[],{"title":2092},{"VI":2093},"Department of Medical Informatics, West China School of Medicine\u002FWest China Hospital, Sichuan University, Chengdu, People’s Republic of China",[],{"title":2096},{"VI":2097},"Rui Zhang",{"id":2099,"sortIndex":96,"researcher":18,"roles":2100,"affiliations":2101,"properties":2124,"displayName":2126,"givenName":18,"familyName":18},"41652001-709e-4159-b18b-0910bb896305",[136],[2102,2108,2116],{"id":2088,"sortIndex":19,"affiliation":2103,"properties":18},{"id":2088,"createTime":18,"updateTime":18,"relativeEntities":2104,"slug":18,"properties":2105,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2107,"statistic":18},[],{"title":2106},{"VI":2093},[],{"id":2109,"sortIndex":19,"affiliation":2110,"properties":18},"e9861d85-25fa-4003-ab01-5cd4b2770ad5",{"id":2109,"createTime":18,"updateTime":18,"relativeEntities":2111,"slug":18,"properties":2112,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2115,"statistic":18},[],{"title":2113},{"VI":2114},"Information Center, West China Hospital, Sichuan University, Chengdu, People’s Republic of China",[],{"id":2117,"sortIndex":19,"affiliation":2118,"properties":18},"48e83ba3-fada-4f03-8f9c-9a6c1162ecb2",{"id":2117,"createTime":18,"updateTime":18,"relativeEntities":2119,"slug":18,"properties":2120,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2123,"statistic":18},[],{"title":2121},{"EN":2122},"Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu, People’s Republic of China",[],{"title":2125},{"VI":2126},"Jialin Liu",{"id":2128,"sortIndex":94,"researcher":18,"roles":2129,"affiliations":2130,"properties":2137,"displayName":2139,"givenName":18,"familyName":18},"4df0339f-938a-487e-a3ef-8aa5dbd8ea28",[136],[2131],{"id":2109,"sortIndex":19,"affiliation":2132,"properties":18},{"id":2109,"createTime":18,"updateTime":18,"relativeEntities":2133,"slug":18,"properties":2134,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2136,"statistic":18},[],{"title":2135},{"VI":2114},[],{"title":2138},{"VI":2139},"Yong Huang",{"id":2141,"sortIndex":95,"researcher":18,"roles":2142,"affiliations":2143,"properties":2150,"displayName":2152,"givenName":18,"familyName":18},"b66954ec-7003-42ba-9c84-2c882dcb2dd2",[136],[2144],{"id":2109,"sortIndex":19,"affiliation":2145,"properties":18},{"id":2109,"createTime":18,"updateTime":18,"relativeEntities":2146,"slug":18,"properties":2147,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2149,"statistic":18},[],{"title":2148},{"VI":2114},[],{"title":2151},{"VI":2152},"Miye Wang",{"id":2154,"sortIndex":357,"researcher":18,"roles":2155,"affiliations":2156,"properties":2163,"displayName":2165,"givenName":18,"familyName":18},"81b481c7-94f0-4e5b-b5bf-9a9e7c21cbe0",[136],[2157],{"id":2109,"sortIndex":19,"affiliation":2158,"properties":18},{"id":2109,"createTime":18,"updateTime":18,"relativeEntities":2159,"slug":18,"properties":2160,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2162,"statistic":18},[],{"title":2161},{"VI":2114},[],{"title":2164},{"VI":2165},"Qingke Shi",{"id":2167,"sortIndex":540,"researcher":18,"roles":2168,"affiliations":2169,"properties":2178,"displayName":2180,"givenName":18,"familyName":18},"936147ed-a0be-46c8-ab20-5fba095d81e9",[136],[2170],{"id":2171,"sortIndex":19,"affiliation":2172,"properties":18},"c0889952-b76e-4416-8b20-0b0ba3ac82b4",{"id":2171,"createTime":18,"updateTime":18,"relativeEntities":2173,"slug":18,"properties":2174,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2177,"statistic":18},[],{"title":2175},{"VI":2176},"Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, People’s Republic of China",[],{"title":2179},{"VI":2180},"Jun Chen",{"id":2182,"sortIndex":558,"researcher":18,"roles":2183,"affiliations":2184,"properties":2199,"displayName":2201,"givenName":18,"familyName":18},"86767cad-8491-438b-9862-c32c4db4c61a",[136],[2185,2191],{"id":2088,"sortIndex":19,"affiliation":2186,"properties":18},{"id":2088,"createTime":18,"updateTime":18,"relativeEntities":2187,"slug":18,"properties":2188,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2190,"statistic":18},[],{"title":2189},{"VI":2093},[],{"id":2192,"sortIndex":19,"affiliation":2193,"properties":18},"164f984d-35e2-4c8e-9071-784e56a90af0",{"id":2192,"createTime":18,"updateTime":18,"relativeEntities":2194,"slug":18,"properties":2195,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2198,"statistic":18},[],{"title":2196},{"VI":2197},"Department of Cardiology, West China Hospital, Sichuan University, Chengdu, People’s Republic of China",[],{"title":2200},{"VI":2201},"Zhi Zeng",{"url":18,"publisher":2203,"properties":18},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":2204,"slug":10,"properties":2205,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":2208,"manageAffiliations":2221,"indexDatabases":2232,"url":91,"thumbnailPath":18,"statistic":2247,"gsStatistic":18,"type":104,"analyzePriority":18},[],{"issn":2206,"title":2207},{"VOID":13},{"EN":15},[2209,2213,2217],{"id":22,"createTime":18,"updateTime":18,"relativeEntities":2210,"label":2211,"description":2212,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":25},{},{"id":28,"createTime":18,"updateTime":18,"relativeEntities":2214,"label":2215,"description":2216,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":31},{},{"id":34,"createTime":18,"updateTime":18,"relativeEntities":2218,"label":2219,"description":2220,"parentId":18,"standard":18,"scholarHubFieldId":18},[],{"EN":37},{},[2222,2227],{"id":41,"createTime":18,"updateTime":18,"relativeEntities":2223,"slug":18,"properties":2224,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2226,"statistic":18},[],{"title":2225},{"EN":45},[],{"id":48,"createTime":18,"updateTime":18,"relativeEntities":2228,"slug":18,"properties":2229,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2231,"statistic":18},[],{"title":2230},{"EN":52},[],[2233,2240],{"id":56,"indexDatabase":2234,"url":67,"indexYears":68,"academicFieldIds":2239,"indexDatabaseRanking":73},{"id":58,"createTime":18,"updateTime":18,"relativeEntities":2235,"label":2236,"description":2237,"key":64,"publicationTags":2238,"standard":18},[],{"EN":61,"VI":61},{"EN":61,"VI":63},[66],[70,71,72],{"id":75,"indexDatabase":2241,"url":88,"indexYears":18,"academicFieldIds":2246,"indexDatabaseRanking":18},{"id":77,"createTime":18,"updateTime":18,"relativeEntities":2242,"label":2243,"description":2244,"key":84,"publicationTags":2245,"standard":18},[],{"EN":80,"VI":80},{"EN":82,"VI":83},[86,87],[90],{"impactFactor":19,"impactFactorByYear":2248,"i10Index":96,"i10IndexLast5Year":19,"totalPublication":97,"totalPublicationByYear":2249,"totalCitation":100,"totalCitationByYear":2250,"totalCitationPerPublication":102,"totalCitationPerPublicationByYear":2251,"hindexLast5Year":96,"hindex":96},{"2010":94,"2011":95},{"2007":96,"2009":96,"2011":96,"2012":96,"2013":94,"2014":94,"2016":94,"2017":94,"2019":95,"2020":95,"2021":99,"2022":95,"2024":94},{"2009":100},{"2009":100},{"total":19,"publishYear":1881,"statisticByYear":2253},{},"2017-05-02",[86,73],{"id":2257,"createTime":2258,"updateTime":2259,"relativeEntities":2260,"slug":2261,"properties":2262,"entityType":125,"verifyStatus":126,"verifyTime":2271,"verifyNote":128,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":2272,"fullTextUrl":18,"authors":2273,"publicationType":220,"publisherRelationship":2412,"citationCount":2465,"citationInfo":2466,"publishDate":2468,"publishYear":277,"citationAnalyzeStatus":17,"lastCitationAnalyze":2259,"indexDatabases":2469,"openAccess":18,"references":2470,"isForceReanalyzing":279},"95f0c9f4-c2d1-4251-8047-94e2603df678","2024-01-09T20:42:22.576+00:00","2026-07-28T07:10:14.131+00:00",[],"Mining-FDA-drug-labels-for-medical-conditions",{"abstract":2263,"title":2265,"gsPaper":2267,"doi":2269},{"EN":2264},"Cincinnati Children’s Hospital Medical Center (CCHMC) has built the initial Natural Language Processing (NLP) component to extract medications with their corresponding medical conditions (Indications, Contraindications, Overdosage, and Adverse Reactions) as triples of medication-related information ([(1) drug name]-[(2) medical condition]-[(3) LOINC section header]) for an intelligent database system, in order to improve patient safety and the quality of health care. The Food and Drug Administration’s (FDA) drug labels are used to demonstrate the feasibility of building the triples as an intelligent database system task. This paper discusses a hybrid NLP system, called AutoMCExtractor, to collect medical conditions (including disease\u002Fdisorder and sign\u002Fsymptom) from drug labels published by the FDA. Altogether, 6,611 medical conditions in a manually-annotated gold standard were used for the system evaluation. The pre-processing step extracted the plain text from XML file and detected eight related LOINC sections (e.g. Adverse Reactions, Warnings and Precautions) for medical condition extraction. Conditional Random Fields (CRF) classifiers, trained on token, linguistic, and semantic features, were then used for medical condition extraction. Lastly, dictionary-based post-processing corrected boundary-detection errors of the CRF step. We evaluated the AutoMCExtractor on manually-annotated FDA drug labels and report the results on both token and span levels. Precision, recall, and F-measure were 0.90, 0.81, and 0.85, respectively, for the span level exact match; for the token-level evaluation, precision, recall, and F-measure were 0.92, 0.73, and 0.82, respectively. The results demonstrate that (1) medical conditions can be extracted from FDA drug labels with high performance; and (2) it is feasible to develop a framework for an intelligent database system.",{"EN":2266},"Mining FDA drug labels for medical 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(Oral communication from SHARPn’s NLP PI)",{},{"id":2593,"text":2594,"url":2595,"identifiers":2596},"16bd10ea-c1e1-4618-8743-eccd906c21a4","Chapman WW, Dowling JN, Hripcsak G: Evaluation of training with an annotation schema for manual annotation of clinical conditions from emergency department reports. Int J Med Inform. 2008, 77 (2): 107-113. 10.1016\u002Fj.ijmedinf.2007.01.002.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1386505607000147",{"doi":2597},"10.1016\u002Fj.ijmedinf.2007.01.002",{"id":1494,"text":2599,"url":1496,"identifiers":2600},"Deleger L, Li Q, Lingren T, Kaiser M, Molnar K, Zhai H, Stoutenborough L, Solti I:Building Gold standard Corpora for Medical Natural Language Processing Tasks.Proceedings of AMIA Annual Symposium. 2012, Chicago, USA, 144-153.",{"doi":1498},{"id":18,"text":2602,"url":2603,"identifiers":2604},"Uzuner O, Solti I, Cadag E: Extracting medication information from clinical text. J Am Med Inform Assoc. 2010, 17 (5): 514-518. 10.1136\u002Fjamia.2010.003947.","https:\u002F\u002Fdoi.org\u002F10.1136\u002Fjamia.2010.003947",{"mag":2605,"pmc":2606,"openalex":2607,"pm":2608,"doi":2609},"2114039834","2995677","W2114039834","20819854","10.1136\u002Fjamia.2010.003947",{"id":1494,"text":2611,"url":1496,"identifiers":2612},"Friedman C, Hripcsak G: Evaluating natural language processors in the clinical domain. Methods Inf Med. 1998, 37 (4–5): 334-344.",{"doi":1498},{"id":18,"text":2614,"url":2615,"identifiers":2616},"Hripcsak G, Rothschild AS: Agreement, the f-measure, and reliability in information retrieval. J Am Med Inform Assoc. 2005, 12 (3): 296-308. 10.1197\u002Fjamia.M1733.","https:\u002F\u002Fdoi.org\u002F10.1197\u002Fjamia.m1733",{"mag":2617,"pmc":2618,"openalex":2619,"pm":2620,"doi":2621},"79139011","1090460","W79139011","15684123","10.1197\u002Fjamia.m1733",{"id":2623,"text":2624,"url":2625,"identifiers":2626},"ec6efade-325f-4877-b4ce-f0d4b8404f2d","Tsai R, Wu S, Chou W, Lin Y, He D, Hsiang J, Sung T, Hsu W: Various criteria in the evaluation of biomedical named entity recognition. BMI Bioinformatics. 2006, 7: 92-100. 10.1186\u002F1471-2105-7-92.","https:\u002F\u002Fbmcbioinformatics.biomedcentral.com\u002Farticles\u002F10.1186\u002F1471-2105-7-92",{"doi":2627},"10.1186\u002F1471-2105-7-92",{"id":18,"text":2629,"url":18,"identifiers":2630},"Noreen EW: Computer-Intensive Methods for Testing Hypotheses: an Introduction. 1989, New York: Wiley",{},{"id":18,"text":2632,"url":2633,"identifiers":2634},"MALLET: A MAchine Learning for Language Toolkit.http:\u002F\u002Fmallet.cs.umass.edu\u002F,","http:\u002F\u002Fmallet.cs.umass.edu\u002F",{},{"id":1494,"text":2636,"url":1496,"identifiers":2637},"Bland JM, Altman DG: Multiple significance tests: the Bonferroni method. BMJ. 1995, 310 (6973): 170-10.1136\u002Fbmj.310.6973.170.",{"doi":1498},{"id":18,"text":2639,"url":2640,"identifiers":2641},"The pre-publication history for this paper can be accessed here:http:\u002F\u002Fwww.biomedcentral.com\u002F1472-6947\u002F13\u002F53\u002Fprepub","http:\u002F\u002Fwww.biomedcentral.com\u002F1472-6947\u002F13\u002F53\u002Fprepub",{},{"id":2643,"createTime":2644,"updateTime":2645,"relativeEntities":2646,"slug":2647,"properties":2648,"entityType":125,"verifyStatus":126,"verifyTime":2659,"verifyNote":128,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":2660,"fullTextUrl":18,"authors":2661,"publicationType":220,"publisherRelationship":2728,"citationCount":18,"citationInfo":18,"publishDate":2783,"publishYear":2784,"citationAnalyzeStatus":2785,"lastCitationAnalyze":2786,"indexDatabases":2787,"openAccess":18,"references":18,"isForceReanalyzing":279},"62ab3464-8128-448d-8172-71fcc7b76889","2024-01-25T15:57:57.905+00:00","2026-07-28T05:12:44.795+00:00",[],"Publication-trends-in-the-medical-informatics-literature-20-years-of-Medical-Informatics-in-MeSH",{"abstract":2649,"title":2651,"gsPaper":2653,"references":2655,"doi":2657},{"EN":2650},"The purpose of this study is to identify publication output, and research areas, as well as descriptively and quantitatively characterize the field of medical informatics through publication trend analysis over a twenty year period (1987–2006). A bibliometric analysis of medical informatics citations indexed in Medline was performed using publication trends, journal frequency, impact factors, MeSH term frequencies and characteristics of citations. There were 77,023 medical informatics articles published during this 20 year period in 4,644 unique journals. The average annual article publication growth rate was 12%. The 50 identified medical informatics MeSH terms are rarely assigned together to the same document and are almost exclusively paired with a non-medical informatics MeSH term, suggesting a strong interdisciplinary trend. Trends in citations, journals, and MeSH categories of medical informatics output for the 20-year period are summarized. Average impact factor scores and weighted average impact factor scores increased over the 20-year period with two notable growth periods. There is a steadily growing presence and increasing visibility of medical informatics literature over the years. Patterns in research output that seem to characterize the historic trends and current components of the field of medical informatics suggest it may be a maturing discipline, and highlight specific journals in which the medical informatics literature appears most frequently, including general medical journals as well as informatics-specific journals.",{"EN":2652},"Publication trends in the medical informatics literature: 20 years of \"Medical Informatics\" in MeSH",{"VOID":2654},"[]",{"VOID":2656},"American Medical Informatics Association: About AMIA, FAQS. 2005, [http:\u002F\u002Fwww.amia.org\u002Finside]\nGreenes RA, Shortliffe EH: Medical informatics. An emerging academic discipline and institutional priority. JAMA. 1990, 263 (8): 1114-1120. 10.1001\u002Fjama.263.8.1114.\nHersh W: Medical informatics: improving health care through information. JAMA. 2002, 288 (16): 1955-8. 10.1001\u002Fjama.288.16.1955.\nBemmel JV: Medical informatics, art or science?. Methods Inf Med. 1996, 35 (3): 157-72.\nMoehr J: Evaluation: salvation or nemesis of medical informatics?. Comput Biol Med. 2002, 32 (3): 113-25. 10.1016\u002FS0010-4825(02)00009-4.\nClayton P: Do medical informaticists pursue legitimate research?. Methods Inf Med. 1996, 35 (3): 194-5.\nWyatt J: Medical informatics, artefacts or science?. Methods Inf Med. 1996, 35 (3): 197-200.\nFriedman C, Abbas U: Is medical informatics a mature science? A review of measurement practice in outcome studies of clinical systems. Int J Med Inform. 2003, 69 (2–3): 261-72. 10.1016\u002FS1386-5056(02)00109-0.\nLutman M: Bibliometric analysis as a measure of scientific output. Br J Audiol. 1992, 26 (6): 323-4. 10.3109\u002F03005369209076653.\nGarfield E: New factors in the evaluation of scientific literature through citation indexing. American Documentation. 1963, 14: 195-201. 10.1002\u002Fasi.5090140304.\nGarfield E: The meaning of the impact factor. Int J Clin Health Psychol. 2003, 3: 363-69.\nGarfield E: Citation indexing–its theory and application in science, technology and humanities. 1972, New York: John Wiley & Sons\nThompson Scientific: ISI Journal Citation Reports., in ISI Web of Knowledge. 2005\nYoung H: Glossary of Library and Information Science. 1983, Chicago: American Library Association\nLewison G, Devey M: Bibliometric methods for the evaluation of arthritis research. Rheumatology (Oxford). 1999, 38 (1): 13-20. 10.1093\u002Frheumatology\u002F38.1.13.\nGeisler E: The Metrics of Science and Technology. 2000, Westport CT: Quorum Books\nPrice D: Networks of scientific papers. Science. 1965, 149: 510-5. 10.1126\u002Fscience.149.3683.510.\nPrice D: Little science, big science. 1963, New York: Columbia University Press\nGarfield E: Science citation index: a new dimension in indexing. 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Phytomedicine. 2008, 15 (8): 566-576. 10.1016\u002Fj.phymed.2008.04.014.\nHasman A, Haux R: Modeling in Biomedical Informatics – An Exploratory Analysis (Part 1). Methods Inf Med. 2006, 45 (6): 638-642.\nMoorman P, Lei Jvd: An inventory of publications on computer-based medical records: an update. Methods Inf Med. 2003, 42 (3): 199-202.\nMendis K: Health informatics research in Australia: retrospective analysis using PubMed. Informatics in Primary Care. 2007, 15: 17-\nOtero P: Evolution of medical informatics in bibliographic databases. Stud Health Technol Inform. 2004, 107 (Pt 1): 301-305.\nAndrews JE: An author co-citation analysis of medical informatics. J Med Libr Assoc. 2003, 91 (1): 47-56.\nSittig D, Kaalaas-Sittig J: A citation analysis of medical informatics journals. Medinfo. 1995, 8 (Pt 2): 1452-6.\nVishwanatham R: Citation analysis in journal rankings: medical informatics in the library and information science literature. 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Proc AMIA Symp. 2000, 590-4.\nThe pre-publication history for this paper can be accessed here:http:\u002F\u002Fwww.biomedcentral.com\u002F1472-6947\u002F9\u002F7\u002Fprepub",{"VOID":2658},"10.1186\u002F1472-6947-9-7","2024-09-04T22:09:20.276+00:00","https:\u002F\u002Fbmcmedinformdecismak.biomedcentral.com\u002Farticles\u002F10.1186\u002F1472-6947-9-7",[2662,2686,2699],{"id":2663,"sortIndex":19,"researcher":18,"roles":2664,"affiliations":2665,"properties":2683,"displayName":2685,"givenName":18,"familyName":18},"0ab7d88d-da8e-461f-ad67-8b88c6a8345c",[136],[2666,2674],{"id":2667,"sortIndex":19,"affiliation":2668,"properties":18},"4ef05c93-10a4-494d-a178-a8e35a21c23d",{"id":2667,"createTime":18,"updateTime":18,"relativeEntities":2669,"slug":18,"properties":2670,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2673,"statistic":18},[],{"title":2671},{"EN":2672},"Department of Medical Education and Biomedical Informatics, University of Washington, Seattle, USA",[],{"id":2675,"sortIndex":96,"affiliation":2676,"properties":2682},"9ac34277-8261-4d20-8f17-750bd93b38af",{"id":2675,"createTime":18,"updateTime":18,"relativeEntities":2677,"slug":18,"properties":2678,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":2681,"statistic":18},[],{"title":2679},{"VI":2680},"Department of Health Services, University of Washington, Seattle, USA",[],{},{"title":2684},{"VI":2685},"Jonathan P 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