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World Journal of Gastrointestinal Pathophysiology 8(2):27–38, 2017.\nSilvester, J., Duerksen, D., Celiac disease. Canadian Medical Association Journal, vol. 185, no.1, 2013.\nRubio-Tapia, A., Lidvigsson, J. F., Branter, T. L., Murray, J. A., and Everhart, J. E., The prevalence of celiac disease in the United States. American Journal of Gastroenterology 107(10):1538–1544, 2012.\nWorld Gastroenterology Organisation, Celiac disease. WGO Global Guidelines:1–29, 2016.\nOberhuber, G., Granditsch, G., and Vogelsang, H., The histopathology of coeliac disease: time for a standardized report scheme for pathologists. European Journal of Gastroenterology & Hepatology 11(10):1185–1194, 1999.\nKoh, J. E. W., Hagiwara, Y., Oh, S. L., Tan, J. H., Ciaccio, E. J., Green, P. H., Lewis, S. K., and Acharya, U. R., Automated diagnosis of celiac disease using DWT and nonlinear features with video capsule endoscopy images. Future Generation Computer Systems 90:86–93, 2019.\nGreen, P. H. R., The role of endoscopy in the diagnosis of celiac disease. Gasteroenterology & Hepatology 10(8):522–524, 2014.\nCiaccio, E. J., Lewis, S. K., Bhagat, G., and Green, P. H., Coeliac disease and the video capsule: what have we learned till now. Annals of Translational Medicine 5(9):197–197, 2017.\nVecsei, A., Fuhrmann, T., Liedlgruber, M., Brunauer, L., Payer, H., and Uhl, A., Automated classification of duodenal imagery in celiac disease using evolved Fourier feature vectors. Computer Methods and Programs in Biomedicine 95:68–78, 2009.\nCiaccio, E. J., Tennyson, C. A., Bhagat, G., Lewis, S. K., and Green, P. H. R., Classification of videocapsule endoscopy image patterns: comparative analysis between patients with celiac disease and normal individuals. Biomedical Engineering 9(44):1–12, 2010.\nCiaccio, E. J., Tennyson, C. A., Lewis, S. K., Krishnareddy, S., Bhagat, G., and Green, P. H., Distinguishing patients with celiac disease by quantitative analysis of video- capsule endoscopy images. Computer Methods and Programs in Biomedicine 100(1):39–48, 2010.\nCiaccio, E. J., Tennyson, C. A., Bhagat, G., Lewis, S. K., and Green, P. H. R., Robustspectral analysis of videocapsule images acquired from celiac disease patients. Biomedical Engineering 10(78):1–14, 2011.\nGadermayr, M., Wimmer, G., Uhl, A., Kogler, H., Vecsei, A., and Merhof, D., Fully-automated CNN-based Computer Aided Celiac Disease Diagnosis. Image Analysis and Processing 6978:467–478, 2011.\nCiaccio, E. J., Tennyson, C. A., Bhagat, G., Lewis, S. K., and Green, P. H. R., Use of basis images for detection and classification of celiac disease. Biomedical Materials Engineering 24:1913–1923, 2014.\nZhou, T., Han, G., Li, B. N., Lin, Z., Ciaccio, E. J., Green, P. H., and Qin, J., Quantitative analysis of patients with celiac disease by video capsule endoscopy: a deep learning method. Computers in Biology and Medicine 85:1–6, 2017.\nCiaccio, E. J., Bhagat, G., Lewis, S. K., and Green, P. H., Extraction and processing of videocapsule data to detect and measure the presence of villous atrophy in celiac disease patients. Computers in Biology and Medicine 78:97–106, 2016.\nCiaccio, E. J., Bhagat, G., Lewis, S. K., and Green, P. H., Recommendations to quantify villous atrophy in video capsule endoscopy images of celiac disease patients. World Journal of Gastrointestinal Endoscopy 8(18):653–662, 2016.\nWinder, S., Hua, G., Brown, M., Picking the best DAISY image descriptors, Image(Rochester, N.Y.), 2009.\nTola, E., Lepetit, V., DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo, IEEE Intelligence on Pattern Analysis and Machine Intelligence, vol.32, no.5, 2010.\nShannon, C. E., A Mathematical Theory of Communication. The Bell System Technical Journal 27(3):379–423, 1948.\nCouceiro, M., Ghamisi, P., Particle Swarm Optimization, Fractional Order Darwinian Particle Swarm Optimization: Applications and Evaluation of an Evolutionary Algorithm, pp.1-10, 2016.\nLowe, D., Distinctive image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, pp.1-28, 2004.\nAcharya, U. R., Faust, O., Kadri, N. A., Siru, J. S., and Yu, W., Automated identification of normal and diabetes heart rate signals using nonlinear measures. Computers in Biology and Medicine 43(10):1523–1529, 2013.\nGuyon, I., and Elisseeff, A., A, An Introduction to Variable and Feature Selection. Journal of Machine Learning 7(8):1157–1182, 2003.\nHua, W. T., and Dougherty, E., Performance of feature-selection methods in the classification of high-dimension data. Pattern Recognition 42:409–424, 2009.\nKohavi, R., and John, G., Wrappers for Feature Subset Selection. Artificial Intelligence 97:1–2, 1997.\nKennedy, J., Eberhart, R., Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks, Perth, WA, Australia, 1995.\nGirdhar, A., Swarm Intelligence and Flocking Behaviour, International Journal of Computer Applications, pp. 975-8887, 2015.\nCortes, C., and Vapnik, V., Support-Vector Networks. Machine Learning 20:273–297, 1995.\nDuda, R. O., Hart, P. E., Stork, D. G., Pattern classification, second edition, John Wiley and Sons, New York, 2001.\nTola, E., Lepetit, V., Fua, P., A fast local descriptor for dense matching, Conference on Computer Vision and Pattern Recognition, Alaska, USA, 2008.\nLee, J. G., Jun, S., Cho, Y. W., Lee, H., Kim, G. B., Seo, J. B., and Kim, N., Deep learning in medical imaging: General overview. Korean Journal of Radiology 18(4):570–584, 2017.\nFaust, O., Hagiwara, Y., Tan, J. H., Oh, S. L., and Acharya, U. R., Deeplearningforhealth- care applications based on physiological signals: A review. Computer Methods and Programs in Biomedicine 161:1–13, 2018.\nLecun, Y., Bengio, Y., and Hinton, G., Deep Learning. Nature 521(7553):436–444, 2015.",{"EN":258},"Celiac disease is a genetically determined disorder of the small intestine, occurring due to an immune response to ingested gluten-containing food. The resulting damage to the small intestinal mucosa hampers nutrient absorption, and is characterized by diarrhea, abdominal pain, and a variety of extra-intestinal manifestations. Invasive and costly methods such as endoscopic biopsy are currently used to diagnose celiac disease. Detection of the disease by histopathologic analysis of biopsies can be challenging due to suboptimal sampling. Video capsule images were obtained from celiac patients and controls for comparison and classification. This study exploits the use of DAISY descriptors to project two-dimensional images onto one-dimensional vectors. Shannon entropy is then used to extract features, after which a particle swarm optimization algorithm coupled with normalization is employed to select the 30 best features for classification. Statistical measures of this paradigm were tabulated. The accuracy, positive predictive value, sensitivity and specificity obtained in distinguishing celiac versus control video capsule images were 89.82%, 89.17%, 94.35% and 83.20% respectively, using the 10-fold cross-validation technique. When employing manual methods rather than the automated means described in this study, technical limitations and inconclusive results may hamper diagnosis. Our findings suggest that the computer-aided detection system presented herein can render diagnostic information, and thus may provide clinicians with an important tool to validate a diagnosis of celiac disease.",{"EN":260},"Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors",{"VOID":262},"10.1007\u002Fs10916-019-1285-6","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10916-019-1285-6",[268,286,299,314,342,354,367,380],{"id":269,"sortIndex":178,"researcher":22,"roles":270,"affiliations":272,"properties":283},"f018858d-88db-432f-b489-8e103ab7ce23",[271],"AUTHOR",[273],{"id":22,"sortIndex":23,"affiliation":274,"properties":22},{"id":275,"createTime":276,"updateTime":277,"relativeEntities":278,"slug":279,"properties":280,"entityType":67,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"a6b2e5df-f1b4-4d61-9dd5-6e61069f788e","2023-12-14T11:48:07.290+00:00","2024-10-12T01:34:02.980+00:00",[],"Department-of-Electronics-and-Computer-Engineering-Ngee-Ann-Polytechnic-Singapore-Singapore",{"title":281},{"VI":282},"Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore",{"title":284},{"VI":285},"Joel Koh En Wei",{"id":287,"sortIndex":288,"researcher":22,"roles":289,"affiliations":290,"properties":296},"9e3ec6fd-a18e-4e55-bbea-6a12e360dfc5",3,[271],[291],{"id":22,"sortIndex":23,"affiliation":292,"properties":22},{"id":275,"createTime":276,"updateTime":277,"relativeEntities":293,"slug":279,"properties":294,"entityType":67,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},[],{"title":295},{"VI":282},{"title":297},{"VI":298},"Shu Lih Oh",{"id":300,"sortIndex":187,"researcher":22,"roles":301,"affiliations":302,"properties":311},"f69c0329-9d0d-442a-a62e-da967388292a",[271],[303],{"id":22,"sortIndex":23,"affiliation":304,"properties":22},{"id":305,"createTime":306,"updateTime":306,"relativeEntities":307,"slug":22,"properties":308,"entityType":67,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"ab7dd1de-66ea-41db-8cbf-3528d8219710","2024-01-13T00:02:00.428+00:00",[],{"title":309},{"VI":310},"Department of Medicine - Celiac Disease Center, Columbia University, New York, USA",{"title":312},{"VI":313},"Suzanne K. 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Rep. 50(5), 2002.\nLathauwer, L., Moor, B., and Vandewalle, J., Fetal electrocardiogram extraction by blind source subspace separation. IEEE Trans. Biomed. Eng 47:5567–572, 2000.\nLiang, H., Lukkarinen, S., and Hartimo, I., Heart sound segmentation algorithm based on heart sound envelogram. Comput. Cardiol., pp. 105–108, 1997.\nGreenfield, M., Hearing the fetal heartbeat. Dr. Spock Website, available at: http:\u002F\u002Fwww.drspock.com\u002Farticle\u002F0,1510,9851,00.html (last accessed Feb. 2006), 2001.\nMcDonnell, E., and Dripps, J. H., Processing and analysis of fetal phonocardiograms. Proc. Annu. Int. Conf. IEEE Eng 1:61–62, 1989.\nIbrahimy, M. I., Ahmed, F., Ali, M. A. M., and Zahedi, E., Real-time signal processing for fetal heart rate monitoring. IEEE Trans. Biomed. Eng 50:2258–261, 2003.\nBurel, G., Blind separation of sources: a nonlinear neural algorithm. Neural Netw 5:6937–947, 1992.\nJutten, C., and Herault, J., Blind separation of sources, part 1: an adaptive algorithm based on neuromimetic architecture. Signal Process 24:11–10, 1991.\nPlatt, J. C., and Faggin, F., Networks for separation of sources that are superimposed and delayed. In: Moody, J., Hanson, S., and Lippman, R. (Eds.), Advances in Neural Information Processing Systems 4Palo Alto: Morgan-Kaufmann, 730–737, 1992.\nDelfosse, N., and Loubaton, P., Adaptive separation of independent sources: a deflation approach. In: Proc. ICASSP, pp. 41–44, Adelaide, Australia, 1994.\nKarhunen, J., Wang, L., and Vigario, R., Nonlinear PCA type approaches for source separation and independent component analysis. In: Proc. ICNN, Perth, Western Australia, 1995.\nCardoso, J. F., Belouchrani, A., and Laheld, B., A new composite criterion for adaptive and iterative blind source separation. In: Proc. ICASSP, pp. 273–276, Adelaide, Australia, 1994.\nComon, P., Independent component analysis, a new concept? Signal Process 36:3287–314, 1994.\nKam, A., and Cohen, A., Separation of twins fetal ECG by means of blind source separation. Electrical and electronic engineers in Israel. The 21st IEEE Convention, pp. 342–345, 2000.\nTorkkola, K., Blind separation of delayed sources based on information maximization. In: Proc. IEEE ICASSP, pp. 3509–3513, 1996.\nKovacs, F., Torok, M., and Habermajer, I., A rule-based phonocardiographic method for long-term fetal heart rate monitoring. IEEE Trans. Biomed. Eng 47:1124–130, 2000.\nBell, A., and Sejnowski, T., An information maximization approach to blind separation and blind deconvolution. Neural Comput 7:61004–1034, 1995.\nNigam, V., and Priemer, R., Blind separation of mixtures of delayed sources. In: Electronic Proceedings of CITSA, Orlando, FL, 2004.\nEmile, B., Comon, P., and Leroux, J., Estimation of time delays with fewer sensors than sources. IEEE Trans. Signal Process 46:72012–2015, 1998.\neGeneralMedical.com, Product information cardiac auscultation of heart murmurs. Available at: http:\u002F\u002Fegeneralmedical.com\u002Flistohearmur.html (last accessed Feb. 2006)\nAmari, S., Cichocki, A., and Yang, H. H., A new learning algorithm for blind signal separation. Advances in neural information processing systems. Vol. 8. Cambridge, MA: MIT, 752–763, 1996.\nMesser, S., and Abbott, D., Optimal wavelet denoising for phonocardiograms. Microelectron. J 32:12931–941, 2001.\nDonoho, D. L., De-noising by soft thresholding. IEEE Trans. Inf. Theory 41:3613–627, 1995.\nNigam, V., and Priemer, R., A snore extraction method from mixed sound for a mobile snore recorder. J. Med. Syst. 30:91–99.",{"EN":691},"The fetal phonocardiogram, which is the acoustic recording of mechanical activity of the fetal heart, facilitates the measurement of the instantaneous fetal heart rate, beat-to-beat differences and duration of systolic and diastolic phases. These measures are sensitive indicators of cardiac function, reflecting fetal well-being. This paper provides an algorithm to non-invasively estimate the phonocardiogram of an individual fetus in a multiple fetus pregnancy. A mixture of fetal phonocardiograms is modeled by a generalized pure delayed mixing model. Mutual independence of fetal phonocardiograms is assumed to apply blind source separation based techniques to extract the fetal phonocardiograms from their mixtures. The performance of the algorithm is verified through simulation results and on experimental data obtained from a phantom that is used to simulate a twin pregnancy.",{"EN":693},"Generalized Blind Delayed Source Separation Model for Online Non-invasive Twin-fetal Sound Separation: A Phantom Study",{"VOID":695},"10.1007\u002Fs10916-007-9115-7","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10916-007-9115-7",[698,713],{"id":699,"sortIndex":178,"researcher":22,"roles":700,"affiliations":701,"properties":710},"42de1582-f053-4206-ba14-3af402b27119",[271],[702],{"id":22,"sortIndex":23,"affiliation":703,"properties":22},{"id":704,"createTime":705,"updateTime":705,"relativeEntities":706,"slug":22,"properties":707,"entityType":67,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"7ecdb87f-1249-4cca-9446-aa13494f3278","2024-01-21T00:02:08.027+00:00",[],{"title":708},{"VI":709},"Electrical and Computer Engineering Department, University of Illinois at Chicago, Chicago, USA",{"title":711},{"VI":712},"Roland Priemer",{"id":714,"sortIndex":23,"researcher":22,"roles":715,"affiliations":716,"properties":725},"c923045a-8c9e-4f1b-b7e6-97251dbc09be",[271],[717],{"id":22,"sortIndex":23,"affiliation":718,"properties":22},{"id":719,"createTime":720,"updateTime":720,"relativeEntities":721,"slug":22,"properties":722,"entityType":67,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"ab1308e1-ed90-4bc6-aa4c-9b9f2dff4333","2024-01-21T00:02:08.015+00:00",[],{"title":723},{"VI":724},"Ikoa, Inc, Menlo Park, USA",{"title":726},{"VI":727},"Vivek 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H, Diesch K, Ganslandt T et al. (2010) Numerical parameters and quality indicators in a medical emergency department. Dtsch. Arztebl. Int. 107(15): 261–267. doi: https:\u002F\u002Fdoi.org\u002F10.3238\u002Farztebl.2010.0261\nNeumayr A, Baubin M, Schinnerl A (eds) (2018) Herausforderung Notfallmedizin. Springer Berlin Heidelberg, Berlin, Heidelberg\nHörster AC, Kulla M, Brammen D et al. (2016) Potential for the survey of quality indicators based on a national emergency department registry: a systematic literature search (Potential for the survey of quality indicators based on a national emergency department registry : A systematic literature search). Med Klin Intensivmed Notfmed doi: https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00063-016-0180-x\nNoel G, Drigues C, Viudes G (2018) Which indicators to include in a crowding scale in an emergency department? A national French Delphi study. Eur J Emerg Med 25(4): 257–263. doi: https:\u002F\u002Fdoi.org\u002F10.1097\u002FMEJ.0000000000000454\nHaugland H, Rehn M, Klepstad P et al. (2017) Developing quality indicators for physician-staffed emergency medical services: a consensus process. Scand J Trauma Resusc Emerg Med 25(1): 14. doi: https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13049-017-0362-4\nMadsen M, Kiuru S, Castrèn M et al. (2015) The level of evidence for emergency department performance indicators: Systematic review. Eur J Emerg Med 22(5): 298–305. doi: https:\u002F\u002Fdoi.org\u002F10.1097\u002FMEJ.0000000000000279\nMadsen MM, Eiset AH, Mackenhauer J et al. (2016) Selection of quality indicators for hospital-based emergency care in Denmark, informed by a modified-Delphi process. Scand J Trauma Resusc Emerg Med 24: 11. doi: https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13049-016-0203-x\nMattsson MS, Oettinger M, Jørsboe H (2013) Health care quality in a new Emergency Department based on the Danish Stroke register data. Scand J Trauma Resusc Emerg Med 21(S2). doi: https:\u002F\u002Fdoi.org\u002F10.1186\u002F1757-7241-21-S2-A27\nMattsson MS, Mattsson N, Jørsboe HB (2014) Improvement of clinical quality indicators through reorganization of the acute care by establishing an emergency department-a register study based on data from national indicators. Scand J Trauma Resusc Emerg Med 22: 60. doi: https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13049-014-0060-4\nLapostolle F, Bataille S, Loyeau A et al. (2019) Decision to deploy coronary reperfusion is not affected by the volume of ST-segment elevation myocardial infarction patients managed by prehospital emergency medical teams. Eur J Emerg Med 26(6): 423–427. doi: https:\u002F\u002Fdoi.org\u002F10.1097\u002FMEJ.0000000000000586\nGorlicki J, Raynal P-A, Leleu A et al. (2015) Reliability of electronic recording of waiting times in the emergency department: a prospective multicenter study. Eur J Emerg Med 22(5): 366–369. doi: https:\u002F\u002Fdoi.org\u002F10.1097\u002FMEJ.0000000000000232\nCenters for Medicare & Medicaid Services (CMS), HHS (2016) Medicare Program; Merit-Based Incentive Payment System (MIPS) and Alternative Payment Model (APM) Incentive Under the Physician Fee Schedule, and Criteria for Physician-Focused Payment Models. Final rule with comment period Fed Regist 81(214): 77008–77831\nSørup CM, Jacobsen P, Forberg JL (2013) Evaluation of emergency department performance - a systematic review on recommended performance and quality-in-care measures. Scand J Trauma Resusc Emerg Med 21: 62. doi: https:\u002F\u002Fdoi.org\u002F10.1186\u002F1757-7241-21-62\nKulla M, Goertler M., Somasundaram R. et al. (2016) Bewertung von Qualitätsindikatoren für die Notaufnahme. Notfall Rettungsmed 19(8): 646–656. doi: https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10049-016-0236-8\nAfilal M, Yalaoui F, Dugardin F et al. (2016) Forecasting the Emergency Department Patients Flow. J Med Syst 40(7): 175. doi: https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10916-016-0527-0\nKadri F, Harrou F, Chaabane S et al. (2014) Time series modelling and forecasting of emergency department overcrowding. J Med Syst 38(9): 107. doi: https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10916-014-0107-0\nObermeyer Z, Emanuel EJ (2016) Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med 375(13): 1216–1219. doi: https:\u002F\u002Fdoi.org\u002F10.1056\u002FNEJMp1606181\nSearle J, Muller R, Slagman A et al. (2015) Überfüllung der Notaufnahmen. Notfall Rettungsmed 18(4): 306–315. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10049-015-0011-2\nStarnes JR, Wanderer JP, Ehrenfeld JM (2015) Metadata from data: identifying holidays from anesthesia data. J Med Syst 39(5): 44. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10916-015-0232-4\nCrilly J, Bost N, Thalib L et al. (2013) Patients who present to the emergency department and leave without being seen: Prevalence, predictors and outcomes. Eur J Emerg Med 20(4): 248–255. doi: https:\u002F\u002Fdoi.org\u002F10.1097\u002FMEJ.0b013e328356fa0e\nKulla M, Baacke M, Schöpke T et al. (2014) Kerndatensatz \"Notaufnahme\" der DIVI. Notfall Rettungsmed 17(8): 671–681. doi: https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10049-014-1860-9\nvan der Linden, M. C., Lindeboom R, de Haan R et al. (2014) Unscheduled return visits to a Dutch inner-city emergency department. Int J Emerg Med 7(1): 23. https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs12245-014-0023-6\nWuerz RC, Milne LW, Eitel DR et al. (2000) Reliability and Validity of a New Five-level Triage Instrument. Acad. Emerg. Med. Off. J. Soc. Acad. Emerg. Med 7(3): 236–242. doi: https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1553-2712.2000.tb01066.x\nHilt H (2013) Triage in der Notaufnahme. Trauma Berufskrankh 15(3): 164–169. doi: https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10039-013-2004-z\nWilk F, Grosse F, Liebel J et al. (2019) Diagnosen einer Zentralen Notaufnahme als Qualitätsindikator. Notfall Rettungsmed https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10049-019-0611-3\nMockel M, Searle J, Muller R et al. (2013) Chief complaints in medical emergencies: do they relate to underlying disease and outcome? The Charité Emergency Medicine Study (CHARITEM). Eur J Emerg Med 20(2): 103–108. doi: https:\u002F\u002Fdoi.org\u002F10.1097\u002FMEJ.0b013e328351e609\nSağbaş EA, Korukoglu S, Balli S (2020) Stress Detection via Keyboard Typing Behaviors by Using Smartphone Sensors and Machine Learning Techniques. J Med Syst 44(4): 68. doi: https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10916-020-1530-z\nWeiss SJ, Derlet R, Arndahl J et al. (2004) Estimating the degree of emergency department overcrowding in academic medical centers: results of the National ED Overcrowding Study (NEDOCS). Acad. Emerg. Med. Off. J. Soc. Acad. Emerg. Med 11(1): 38–50. doi: https:\u002F\u002Fdoi.org\u002F10.1197\u002Fj.aem.2003.07.017\nKhatri KL, Tamil L (2017) Early detection of peak demand days of chronic respiratory diseases emergency department visits using artificial neural networks. IEEE J Biomed Health Inform https:\u002F\u002Fdoi.org\u002F10.1109\u002FJBHI.2017.2698418",{"EN":775},"Emergency departments need to continuously calculate quality indicators in order to perform structural improvements, improvements in the daily routine, and ad-hoc improvements in everyday life. However, many different actors across multiple disciplines collaborate to provide emergency care. Hence, patient-related data is stored in several information systems, which in turn makes the calculation of quality indicators more difficult. To address this issue, we aim to link and use routinely collected data of the different actors within the emergency care continuum. In order to assess the feasibility of linking and using routinely collected data for quality indicators and whether this approach adds value to the assessment of emergency care quality, we conducted a single case study in a German academic teaching hospital. We analyzed the available data of the existing information systems in the emergency continuum and linked and pre-processed the data. Based on this, we then calculated four quality indicators (Left Without Been Seen, Unplanned Reattendance, Diagnostic Efficiency, and Overload Closure). Lessons learned from the calculation and results of the discussions with staff members that had multiple years of work experience in the emergency department provide a better understanding of the quality of the emergency department, the related challenges during the calculation, and the added value of linking routinely collected data.",{"EN":777},"Assessing healthcare service quality using routinely collected data: Linking information systems in emergency 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L., The distributed processing approach to hospital information processing.14th Hawaii International Conference on Systems Sciences, January 1981.\nTolchin, S. G., and Stewart, R. L., The distributed processing approach to hospital information processing.J. Med. Syst. 5:345–360, 1981.\nStewart, R. L., and Tolchin, S. G., A distributed processing\u002Ffiber-optic hospital information system.Fourth Annual Symposium on Computers in Medical Care, November 1980.\nTolchin, S. G., Blum, B. I., and Butterfield, M. A., A systems analysis methodology for a decentralized HIS.Fourth Annual Symposium on Computers in Medical Care, November 1980.\nZeichner, M. L., Brusil, P. J., and Tolchin, S. G., Distributed processing architecture for a hospital information system.Third Annual Symposium on Computers in Medical Care, October 1979.\nWhiting-O'Keefe, Q. E., Simborg, D. W., and Tolchin, S. G., The argument for mocular distributed hospital information systems.Fifth Annual Symposium on Computers in Medical Care, November 1981.\nKahn, S. A., Stewart, R. L., Tolchin, S. G., and Healy, S. J., Functional and logical description of a new fiber-optic contention bus network.COMPCON Fall 80, September 1980.\nWood, D. C., et al., A cable-bus protocol architecture.Sixth Data Communications Symposium, November 1979, pp. 137–146.\nCarpenter, R. J., et al, A microprocessor-based local network node.IEEE COMPCON, September 1978, pp. 104–109.\nMetcalfe, R. M., and Boggs, D. R., Ethernet: Distributed packet switching for local computer networks.Comm. ACM, July 1976, pp. 395–404.\nPolicy Implications of Medical Information Systems, Office of Technology Assessment, U. S. Congress, Government Printing Office, Stock No. 052-003-00496-8, Washington, D. C., 1977.\nKomaroff, A. L., The variability and inaccuracy of medical data.Proc. IEEE 67(9):1196–1207, 1979.\nPayne, L. C., Brown, P. T. 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Int J Med Inform. 83(9):655–669, 2014.\nRosenthal, A. et al., Cloud computing: A new business paradigm for Biomedical information sharing. J Biomed Inform. 43(2):342–353, 2010.\nMy Health Bank can promote self-health mangement, Ministry of Health and Welfare News website. https:\u002F\u002Fwww.nhi.gov.tw\u002FNews_Content.aspx?n=FC05EB85BD57C709&sms=587F1A3D9A03E2AD&s=5AFC4D64B1494D67, Published March 1, 2016.\nFerner, R. E., and Aronson, J. K., Clarification of terminology in medication errors. Drug safety. 29(11):1011–1022, 2006.\nWorld-Health-Organization, Reporting and learning systems for medication errors: the role of pharmacovigilance centres. Geneva, Switzerland: World Health Organization, 2014.\nThe drug-relief report, Taiwan-Drug-Relief-Foundation website. http:\u002F\u002Fwww.tdrf.org.tw\u002Ffiles\u002Ffiles\u002F1_until%20may.pdf, Published may 1, 2016.\nAgrawal, A., Medication errors: prevention using information technology systems. Brit J Clin Pharmaco. 67(6):681–686, 2009.\nHsieh, P.J., Lai, H.M., and Hong, Y.L. Explaining Physicians’ Acceptance and Resistance to the NHI Pharmacloud: A Theoretical Model and Empirical Test. in PACIS, 2015.\nHuang, S. K. et al., NHI-PharmaCloud in Taiwan—A preliminary evaluation using the RE-AIM framework and lessons learned. Int J Med Inform. 84(10):817–825, 2015.\nHsieh, P. J., and Lin, W.S., Explaining resistance to system usage in the PharmaCloud: A view of the dual-factor model. Inf. Manag. 55(1):51–63, 2018.\nKuo, A., Opportunities and Challenges of Cloud Computing to Improve Health Care Services. J Med Internet Res 13(3):e67, 2011.\nNur, F. N., and Moon, N. N., Health care system based on cloud computing. Asian Transactions on Computers. 2(5):9–11, 2012.\nMathew, S., Cloud computing: a new foundation towards health care. International Journal of Innovative Technology and Exploring Engineering. 3(2):118–121, 2013.\nDavis, F. 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Appl Ergon. 37(3):283–295, 2006.\nMathieson, K., Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Inform Syst Res. 2(3):173–191, 1991.\nTaylor, S., and Todd, P. A., Understanding information technology usage: A test of competing models. Inform Syst Res. 6(2):144–176, 1995.\nGavaza, P. et al., Examination of pharmacists’ intention to report serious adverse drug events (ADEs) to the FDA using the theory of planned behavior. Research in Social and Administrative Pharmacy. 7(4):369–382, 2011.\nMoore, G. C., and Benbasat, I., Development of an instrument to measure the perceptions of adopting an information technology innovation. Inform Syst Res. 2(3):192–222, 1991.\nPfeffer, J., Organizations and organization theory. Boston: Pitman, 1982.\nMakowsky, M. J., Guirguis, L. M., Hughes, C. A., Sadowski, C. 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Inform Manage. 43(5):565–571, 2006.\nBandura, A., Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 84(2):191, 1977.\nBandura, A., Health promotion by social cognitive means. Health Educ Behav. 31(2):143–164, 2004.\nRimal, R. N., Perceived risk and self-efficacy as motivators: Understanding individuals' long-term use of health information. J Commun. 51(4):633–654, 2001.\nGist, M. E., and Mitchell, T. R., Self-efficacy: A theoretical analysis of its determinants and malleability. Acad Manage Rev. 17(2):183–211, 1992.\nWang, Y. S., Wang, Y. M., Lin, H. H., and Tang, T. I., Determinants of user acceptance of Internet banking: an empirical study. Int J Serv Ind Manag. 14(5):501–519, 2003.\nBauer, R. A., Consumer behavior and risk taking in risk taking and information handling in consumer behavior, Edited by: Donald F. Cox. Cambride, USA: Harvard University Press, 1967.\nDowling, G. R., and Staelin, R., A model of perceived risk and intended risk-handling activity. J Consum Res. 21(1):119–134, 1994.\nIgbaria, M., User acceptance of microcomputer technology: an empirical test. Omega. 21(1):73–90, 1993.\nCarroll, N. V., Siridhara, C., and Fincham, J. E., Perceived risks and pharmacists' generic substitution behavior. J Consum Aff. 20(1):36–47, 1986.\nCunningham, S. M., The major dimensions of perceived risk. Risk taking and information Handling in consumer behavior. 1:82–111, 1967.\nPeterson, G., Wu, M., and Bergin, J., Pharmacists’ attitudes towards dispensing errors: their causes and prevention. J Clin Pharm Ther. 24(1):57–71, 1999.\nAspden, P., Corrigan, J. M., Wolcott, J., and Erickson, S. M., Patient safety: achieving a new standard for care. Washington, DC, USA: National Academies Press, 2004.\nFeatherman, M. S., and Pavlou, P. A., Predicting e-services adoption: a perceived risk facets perspective. Int J Hum-Compu St. 59(4):451–474, 2003.\nSalahuddin, L., and Ismail, Z., Classification of antecedents towards safety use of health information technology: A systematic review. Int J Med Inform. 84(11):877–891, 2015.\nNunnally, J., Psychometric methods. New York: McGraw-Hill, 1978.\nAnderson, J. C., and Gerbing, D. W., Structural equation modeling in practice: A review and recommended two-step approach. Psycho Bull. 103(3):411, 1988.\nFornell, C., and Bookstein, F. L., Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. J. Mark. Res. 19(4):440–452, 1982.\nSeyal, A. H., Rahman, M. N. A., and Rahim, M. M., Determinants of academic use of the Internet: a structural equation model. Behav Inform Technol. 21(1):71–86, 2002.\nHair, J. F., Anderson, R. E., Tatham, R. L., and Black, W. C., Multivariate data analysis, 5th. New York: Prentice Hall International, 1998.",{"EN":1424},"Taiwan’s National Health Insurance (NHI) is one of the most successful insurance programs in the world. The National Health Insurance Administration (NHIA) established the NHI-PharmaCloud as a platform to reduce medication duplication and other medication errors among the NHI-contracted facilities. The NHI-PharmaCloud can help pharmacists access patient medication information from the preceding 3 months to improve drug safety. The use of NHI-PharmaCloud can improve the quality of healthcare, but improvements cannot occur if pharmacists are unwilling to use the platform. Therefore, the main objective of our study is to investigate the factors affecting pharmacists’ adoption of the NHI-PharmaCloud. This study develops a research model using theories of technology adoption, self-efficacy, and perceived risk and uses randomly distributed survey questionnaires to collect data from local pharmacists. The results show that self-efficacy, perceived usefulness, and perceived psychological risk are 3 critical factors that could affect pharmacists’ willingness to use the NHI-PharmaCloud. The research results may also help NHIA to effectively promote the usage of the NHI-PharmaCloud in Taiwan. In addition, governments in other countries may refer to the results of this study when implementing their own PharmaCloud-type systems to improve drug safety.",{"EN":1426},"An Investigation of Pharmacists’ Acceptance of NHI-PharmaCloud in Taiwan",{"VOID":1428},"10.1007\u002Fs10916-018-1017-3","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10916-018-1017-3",[1431,1451],{"id":1432,"sortIndex":23,"researcher":22,"roles":1433,"affiliations":1434,"properties":1448},"2cb5819d-bfe2-4e4e-823e-a781d1a54bf1",[271],[1435],{"id":1436,"sortIndex":23,"affiliation":1437,"properties":1446},"ce368324-61ab-4b7a-a18b-19591e753836",{"id":1438,"createTime":1439,"updateTime":1440,"relativeEntities":1441,"slug":1442,"properties":1443,"entityType":67,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"08a90605-def4-41a7-8936-0c430b7cdefb","2024-02-12T02:34:31.473+00:00","2025-06-11T21:38:37.652+00:00",[],"National-Kaohsiung-First-University-of-Science-and-Technology-Kaohsiung-Taiwan",{"title":1444},{"VI":1445},"National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan",{"title":1447},{"VI":1445},{"title":1449},{"VI":1450},"Ching-Chang Lee",{"id":1452,"sortIndex":23,"researcher":22,"roles":1453,"affiliations":1454,"properties":1460},"236c3661-ad8a-40ae-9cdb-280579a6ebac",[271],[1455],{"id":22,"sortIndex":23,"affiliation":1456,"properties":22},{"id":1438,"createTime":1439,"updateTime":1440,"relativeEntities":1457,"slug":1442,"properties":1458,"entityType":67,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},[],{"title":1459},{"VI":1445},{"title":1461},{"VI":1462},"Meng-Chi Liu",{"url":1429,"publisher":1464,"properties":1493},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1465,"slug":10,"properties":1466,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":1471,"manageAffiliations":1472,"indexDatabases":1473,"url":22,"thumbnailPath":22,"statistic":1488,"gsStatistic":22,"type":243,"analyzePriority":22},[],{"issn":1467,"eissn":1468,"title":1469,"url":1470},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},[],[],[1474,1481],{"id":82,"indexDatabase":1475,"url":95,"indexYears":96,"academicFieldIds":1480,"indexDatabaseRanking":102},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":1476,"label":1477,"description":1478,"key":92,"publicationTags":1479,"standard":22},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99,100,101],{"id":104,"indexDatabase":1482,"url":119,"indexYears":22,"academicFieldIds":1487,"indexDatabaseRanking":22},{"id":106,"createTime":107,"updateTime":108,"relativeEntities":1483,"label":1484,"description":1485,"key":115,"publicationTags":1486,"standard":22},[],{"EN":111,"VI":111},{"VI":113,"EN":114},[117,118],[121,122],{"impactFactor":23,"impactFactorByYear":1489,"i10Index":137,"i10IndexLast5Year":138,"totalPublication":139,"totalPublicationByYear":1490,"totalCitation":174,"totalCitationByYear":1491,"totalCitationPerPublication":207,"totalCitationPerPublicationByYear":1492,"hindexLast5Year":157,"hindex":157},{"2012":125,"2013":126,"2014":127,"2015":128,"2016":129,"2017":130,"2018":131,"2019":132,"2020":133,"2021":134,"2022":135,"2023":136},{"1977":141,"1978":141,"1979":142,"1980":143,"1981":144,"1982":138,"1983":145,"1984":146,"1985":147,"1986":144,"1987":148,"1988":148,"1989":149,"1990":150,"1991":147,"1992":151,"1993":152,"1994":153,"1995":154,"1996":147,"1997":144,"1998":149,"1999":145,"2000":153,"2001":151,"2002":149,"2003":152,"2004":152,"2005":155,"2006":156,"2007":157,"2008":158,"2009":159,"2010":160,"2011":161,"2012":162,"2013":163,"2014":164,"2015":165,"2016":166,"2017":165,"2018":167,"2019":168,"2020":169,"2021":170,"2022":171,"2023":172,"2024":173},{"1977":176,"1978":177,"1979":178,"1981":179,"1982":180,"1983":181,"1984":182,"1985":183,"1986":184,"1987":185,"1988":186,"1989":148,"1990":187,"1991":148,"1992":188,"1993":146,"1994":189,"1995":155,"1996":190,"2004":191,"2005":144,"2006":192,"2007":193,"2008":194,"2009":195,"2010":196,"2011":197,"2012":198,"2013":199,"2014":200,"2015":201,"2016":202,"2017":203,"2018":204,"2019":205,"2020":206,"2021":170,"2022":138},{"1977":209,"1978":210,"1979":211,"1981":210,"1982":212,"1983":213,"1984":129,"1985":214,"1986":215,"1987":216,"1988":217,"1989":218,"1990":219,"1991":220,"1992":221,"1993":222,"1994":223,"1995":224,"1996":225,"2004":226,"2005":227,"2006":228,"2007":229,"2008":230,"2009":231,"2010":228,"2011":232,"2012":233,"2013":234,"2014":235,"2015":236,"2016":237,"2017":238,"2018":239,"2019":240,"2020":241,"2021":178,"2022":242},{"volume":1494,"pages":1496},{"VOID":1495},"42",{"VOID":1497},"1-11","2018-09-28",2018,{"id":1501,"createTime":1502,"updateTime":1502,"relativeEntities":1503,"slug":22,"properties":1504,"entityType":263,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":1513,"fullTextUrl":22,"authors":1514,"publicationType":416,"publisherRelationship":1530,"citationCount":22,"citationInfo":22,"publishDate":1564,"publishYear":1413,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":22,"isForceReanalyzing":454},"6cf22ab3-8bec-4f51-bae1-3cfafdc8813e","2024-01-10T23:55:55.286+00:00",[],{"references":1505,"abstract":1507,"title":1509,"doi":1511},{"VOID":1506},"Bloch, F.,Phys. Rev. 70:460, 1946; Bloch, F., Hansen, W. W, and Packard, M.,Phys. Rev. 70:474, 1946.\nPurcell, E.M., Torrey, H.C., and Pound, R.V.,Phys. Rev. 69:37, 1946.\nBratton, C.B., Hopkins, A.L., and Weinberg, J.W.,Science 147:738, 1965.\nLigon, T.R., Master's thesis, Oklahoma State University, 1967.\nJackson, J.A., and Langham, W.H.,Rev. Sci. Instr. 39:510, 1968.\nDamadian, R.,Science 171: 151, 1971.\nLauterbur, P.C.,Bull. Am. Phys. Soc., Ser. II, 18:86, 1972;Nature 242:190, 1973.\nLauterbur, P.C., Mendonca Dias, M.H., and Rudin, A.M.,Frontiers of Biological Energetics (P.O. Dutton, J.S. Leigh, and A. Scarpa, eds.), Academic Press, New York, 1978, p. 752.\nHinshaw, W.S.,Phys. Letters 48A:87, 1974.\nDamadian, R.,Phil. Trans. R. Soc. London B289:489, 1980.\nHinshaw, W.S., Andrew, E.R., Bottomley, P.A., Holland, G.N., Moore, W.S., and Worthington, B.S.,Br. J. Radiol 52:36, 1979.\nMansfield, P., Morris, P.G., Ordidge, R.J., Pykett, I.L., Bangert, V., and Coupland, R.E.,Phil. Trans. R. Soc. London B289:503, 1980.\nEdelstein, W.A., Hutchison, J.M.S., Johnson, G., and Redpath, T.,Phys. Med. Biol. 25:751, 1980.\nHawkes, R.C., Holland, G.N., Moore, W.S., and Worthington, B.S.,J. Comput. Assist. Tomogr. 4:577, 1980.\nLauterbur, P.C., and Lai, C.-M,I.E.E.E. Trans. Nucl. Sci. NS-27: 1227, 1980; Lai, C.-M, and Lauterbur, P.C.,Phys. Med. Biol. 26:851, 1981.\nHerman, G.T., Kramer, D.M., Lauterbur, P.C., Rudin, A.M., Schneider, J.S., and Udupa, J.K., Application of optical instrumentation in medicine IX.Proc. Soc. Photo-Opt. Instr. Eng. 273:35, 1981.\nSimon, H.E., Application of optical instrumentation in medicine IX.Proc. Soc. Photo-Opt. Instr. Eng. 273:41, 1981.\nBrunner, P., and Ernst, R.R.,J. Magn. Reson. 33:83, 1979.\nKasturi, S.R., Ranade, S.S., and Shah, S.S.,Proc. Indian Acad. Sci. 84B:60, 1976; Fruchter, R.G., Goldsmith, M., Boyce, J.G., Nicastri, A.D., Koutcher, J., and Damadian, R.,Gynecol. Oncol. 6:243, 1978.\nSwartz, H.M.,J. Magn. Reson. 29:393, 1978 1:27, 1979.\nSinger, J.R.,Nuclear Magnetic Resonance Imaging in Medicine (L. Kaufman, L.E. Crooks, and A.R. Margulis, eds.), Igaku-Shoin, New York, 1981.\nCrooks, L., Hoennigen, J., Arakawa, M., Kaufman, L., McRee, R., Watts, J., and Singer, J.R.,SPIE Proc. 206,Recent and Future Developments in Medical Imaging II:120, 1979; Kramer, D.M.,Nuclear Magnetic Resonance Imaging in Medicine (L. Kaufman, L.E. Crooks, and A.R. Margulis, eds.), Igaku-Shoin, New York, 1981.",{"EN":1508},"The history of the application of nuclear magnetic resonance techniques to biology and medicine is outlined, and the fundamental principles and certain techniques of nuclear magnetic resonance zeugmatographic imaging are described, with emphasis on true three-dimensional reconstruction methods.",{"EN":1510},"NMR zeugmatographic imaging in medicine",{"VOID":1512},"10.1007\u002FBF00995509","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF00995509",[1515],{"id":1516,"sortIndex":23,"researcher":22,"roles":1517,"affiliations":1518,"properties":1527},"db3a116b-3815-414f-9f50-3a4f02380f9a",[271],[1519],{"id":22,"sortIndex":23,"affiliation":1520,"properties":22},{"id":1521,"createTime":1522,"updateTime":1522,"relativeEntities":1523,"slug":22,"properties":1524,"entityType":67,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23},"a3c187f6-67e2-4978-968a-ae3a37c6ba0f","2024-01-10T23:55:55.310+00:00",[],{"title":1525},{"VI":1526},"From the Departments of Chemistry and Radiology, State University of New York at Stony Brook, Stony Brook",{"title":1528},{"VI":1529},"Paul C. Lauterbur",{"url":1513,"publisher":1531,"properties":1560},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1532,"slug":10,"properties":1533,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":1538,"manageAffiliations":1539,"indexDatabases":1540,"url":22,"thumbnailPath":22,"statistic":1555,"gsStatistic":22,"type":243,"analyzePriority":22},[],{"issn":1534,"eissn":1535,"title":1536,"url":1537},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},[],[],[1541,1548],{"id":82,"indexDatabase":1542,"url":95,"indexYears":96,"academicFieldIds":1547,"indexDatabaseRanking":102},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":1543,"label":1544,"description":1545,"key":92,"publicationTags":1546,"standard":22},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99,100,101],{"id":104,"indexDatabase":1549,"url":119,"indexYears":22,"academicFieldIds":1554,"indexDatabaseRanking":22},{"id":106,"createTime":107,"updateTime":108,"relativeEntities":1550,"label":1551,"description":1552,"key":115,"publicationTags":1553,"standard":22},[],{"EN":111,"VI":111},{"VI":113,"EN":114},[117,118],[121,122],{"impactFactor":23,"impactFactorByYear":1556,"i10Index":137,"i10IndexLast5Year":138,"totalPublication":139,"totalPublicationByYear":1557,"totalCitation":174,"totalCitationByYear":1558,"totalCitationPerPublication":207,"totalCitationPerPublicationByYear":1559,"hindexLast5Year":157,"hindex":157},{"2012":125,"2013":126,"2014":127,"2015":128,"2016":129,"2017":130,"2018":131,"2019":132,"2020":133,"2021":134,"2022":135,"2023":136},{"1977":141,"1978":141,"1979":142,"1980":143,"1981":144,"1982":138,"1983":145,"1984":146,"1985":147,"1986":144,"1987":148,"1988":148,"1989":149,"1990":150,"1991":147,"1992":151,"1993":152,"1994":153,"1995":154,"1996":147,"1997":144,"1998":149,"1999":145,"2000":153,"2001":151,"2002":149,"2003":152,"2004":152,"2005":155,"2006":156,"2007":157,"2008":158,"2009":159,"2010":160,"2011":161,"2012":162,"2013":163,"2014":164,"2015":165,"2016":166,"2017":165,"2018":167,"2019":168,"2020":169,"2021":170,"2022":171,"2023":172,"2024":173},{"1977":176,"1978":177,"1979":178,"1981":179,"1982":180,"1983":181,"1984":182,"1985":183,"1986":184,"1987":185,"1988":186,"1989":148,"1990":187,"1991":148,"1992":188,"1993":146,"1994":189,"1995":155,"1996":190,"2004":191,"2005":144,"2006":192,"2007":193,"2008":194,"2009":195,"2010":196,"2011":197,"2012":198,"2013":199,"2014":200,"2015":201,"2016":202,"2017":203,"2018":204,"2019":205,"2020":206,"2021":170,"2022":138},{"1977":209,"1978":210,"1979":211,"1981":210,"1982":212,"1983":213,"1984":129,"1985":214,"1986":215,"1987":216,"1988":217,"1989":218,"1990":219,"1991":220,"1992":221,"1993":222,"1994":223,"1995":224,"1996":225,"2004":226,"2005":227,"2006":228,"2007":229,"2008":230,"2009":231,"2010":228,"2011":232,"2012":233,"2013":234,"2014":235,"2015":236,"2016":237,"2017":238,"2018":239,"2019":240,"2020":241,"2021":178,"2022":242},{"volume":1561,"pages":1562},{"VOID":1409},{"VOID":1563},"591-597","1982-12-01",{"id":1566,"createTime":1567,"updateTime":1568,"relativeEntities":1569,"slug":1570,"properties":1571,"entityType":263,"verifyStatus":21,"verifyTime":1580,"verifyNote":1581,"syncStatus":21,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":1582,"fullTextUrl":22,"authors":1583,"publicationType":416,"publisherRelationship":1608,"citationCount":22,"citationInfo":22,"publishDate":1642,"publishYear":453,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":22,"isForceReanalyzing":454},"d50439f9-c2c3-4893-9b7b-e08e24ed54f5","2024-01-14T06:25:01.318+00:00","2025-01-14T23:55:31.889+00:00",[],"Clinical-Study-of-Diffusion-Weighted-Imaging-in-the-Diagnosis-of-Liver-Focal-Lesion",{"references":1572,"abstract":1574,"title":1576,"doi":1578},{"VOID":1573},"Christ P F, Ettlinger F, Felix Grün, et al. Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks[J]. 2017.\nRaj, A., and Juluru, K., Visualization and segmentation of liver tumors using dynamic contrast MRI[J]. Conf Proc IEEE Eng Med Biol Soc 2009:6985–6989, 2009.\nMarcan, M., Pavliha, D., Music, M. M. et al., Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver[J]. Radiol Oncol 48(3):267–281, 2014.\nRaj, A., and Juluru, K., Visualization and segmentation of liver tumors using dynamic contrast MRI[C]\u002F\u002F international conference of the IEEE engineering in Medicine & Biology Society. IEEE, 2009.\nGoceri, E., Unlu, M. Z., Guzelis, C. et al., An automatic level set based liver segmentation from MRI data sets[C]\u002F\u002F international conference on image processing theory. IEEE, 2013.\nV. Vezhnevets and V. Konouchine, “GrowCut - Interative multi-label N-D image segmentation,” in Proceedings of Graphicon (Graphicon Scientifc Society, Novosibirsk, Russia, 2005), pp. 150–156.\nK. H. Pohl, J. Fisher, J. J. Levitt, M. E. Shenton, R. Kikinis, W. E. L. Grimson, and W. M. Wells, “A unifying approach to registration, segmentation, and intensity correction,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Palm Springs, FL, Springer, 2005), pp. 310–318.\nF. Wang and B. C. Vemuri, “Simultaneous registration and segmentation of anatomical structures from brain MRI,” in Proceedings of Medical Image Computing and Computer-Assisted Intervention (Palm Springs, FL, Springer, 2005), pp. 17–25.\nManikis, G. C., Marias, K., Dmj, L. et al., Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models[J]. PLoS One 12(9):e0184197, 2017.\nHuang, J., Luo, J., Peng, J. et al., Cerebral schistosomiasis: Diffusion-weighted imaging helps to differentiate from brain glioma and metastasis[J]. Acta Radiol:028418511668717, 2017.\nZhou, S., Yi, Y., and Xu, L., Comments on “Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions”[J]. Magn Reson Imaging 36:175–179, 2017.\nKim, B., Lee, S. S., Sung, Y. S. et al., Intravoxel incoherent motion diffusion-weighted imaging of the pancreas: Characterization of benign and malignant pancreatic pathologies[J]. J Magn Reson Imaging 45(1), 2017.\nKajian XIA, Jiangqiang WANG, Yue WU. Robust Alzheimer Disease classification based on Feature Integration Fusion Model for Magnetic.Journal of Journal of medical imaging and health informatics, vol.7,1-6,2017\nNam, H., and Park, H.-J., Distortion correction of high b-valued and high angular resolution diffusion images using iterative simulated images. NeuroImage 57:968–978, 2011.\nKober, T., Gruetler, R., and Krueger, G., Prospective and retrospective motion correction in diffusion magnetic resonance imaging of the human brain. NeuroImage 59:389–398, 2012.\nHamm, J., Ye, D. H., Verma, R., and Davatzikos, C., GRAM: A framework for geodesic registration on anatomical manifolds. Med Image Anal 14:633–642, 2010.\nTaoli, B., and Koh, D. M., Diusion-weighted MR imaging of the liver. Radiology 254:47–66, 2010.\nMa, D., Lu, F., Zou, X. et al., Intravoxel incoherent motion diffusion-weighted imaging as an adjunct to dynamic contrast-enhanced MRI to improve accuracy of the differential diagnosis of benign and malignant breast lesions.[J]. Magn Reson Imaging 36:175–179, 2017.\nKlein, S., Staring, M., Murphy, K., Viergever, M. A., and Pluim, J. P. W., Elastix: A tool for intensity based medical image registration. IEEE Trans Med Imaging 29:196–205, 2010.\nMattes, D., Haynor, D. R., Vesselle, H., Lewellen, T. K., and Eubank, W., PETCT image registration in the chest using free-form deformations. IEEE Trans Med Imaging 22:120–128, 2003.\nEgger, J., Kapur, T., Fedorov, A., Miller, J. V., Veeraraghavan, H., Freisleben, B., Golby, A. J., Nimsky, C., and Kikinis, R., GBM volumetry using the 3D slicer medical image computing platform. Sci Rep 3:1364–1370, 2013.\nRabasco, P., Caivano, R., Simeon, V. et al., Can diffusion-weighted imaging and related apparent diffusion coefficient be a prognostic value in women with breast Cancer?[J]. Cancer Investig 35(2):8, 2017.\nPratiksha, Y., and Surbhi, C., Effectivity of combined diffusion-weighted imaging and contrast-enhanced MRI in malignant and benign breast lesions[J]. Pol J Radiol 83:82–93, 2018.",{"EN":1575},"Apparent diffusion coefficient (ADC), derived from diffusion-weighted magnetic resonance images (DW-MRI), measures the motion of water molecules in vivo and can be used to quantify tumor response so as to determine the best therapy approach. In this paper, our goal was to determine whether the DW-MRI can be used for qualitative and quantitative liver cancer analysis, where an automated method will be proposed for improving the accuracy of liver segmentation in DW-MRI to increase the ability of diagnosis of disease. We firstly analyzed the research status of liver cancer diagnosis, especially on the issues of liver image segmentation technology in MRI. Then, the imaging mechanism and image features of the DW-MRI were analyzed, and the initial DW-MRI slice was segmented by graph-cut algorithm. Finally, our obtained result from the liver DW-MRI image is quantitatively and qualitatively analyzed. Experimental results show that DW-MRI has a great advantage in the diagnosis, the DWI images of benign lesion group was lower than that of malignant lesion, thus DW-MRI is segmented by graph-cut algorithm can provide important additional information regarding differential diagnosis of specific liver cancer to some extend.",{"EN":1577},"Clinical Study of Diffusion-Weighted Imaging in the Diagnosis of Liver Focal Lesion",{"VOID":1579},"10.1007\u002Fs10916-019-1164-1","2025-01-14T23:55:31.888+00:00","Author affiliation is 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