Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards
Tóm tắt
Từ khóa
Tài liệu tham khảo
Mok, 2015, Attitudes towards vital signs monitoring in the detection of clinical deterioration: scale development and survey of ward nurses, Int J Qual Health Care, 27, 207, 10.1093/intqhc/mzv019
Hands, 2013, Patterns in the recording of vital signs and early warning scores: compliance with a clinical escalation protocol, BMJ Qual Saf, 22, 719, 10.1136/bmjqs-2013-001954
Kyriacos, 2014, Monitoring vital signs: development of a modified early warning scoring (MEWS) system for general wards in a developing country, PLOS ONE, 9, e87073, 10.1371/journal.pone.0087073
Mell, 2010, The NIST definition of cloud computing, Commun ACM, 53, 50
Mitchell, 1997
Meier, 2013, eHealth: extending, enhancing, and evolving health care, Annu Rev Biomed Eng, 15, 359, 10.1146/annurev-bioeng-071812-152350
Becker, 2014, mHealth 2.0: experiences, possibilities, and perspectives, JMIR Mhealth Uhealth, 2, 1, 10.2196/mhealth.3328
da Costa, 2008, Toward a general software infrastructure for ubiquitous computing, IEEE Pervas Comput, 7, 64, 10.1109/MPRV.2008.21
Jung, 2013, Home health gateway based healthcare services through u-health platform, Wirel Pers Commun, 73, 207, 10.1007/s11277-013-1231-8
De Sanctis, 2016, Personalized ubiquitous health and wellness care: The KNOWLEDGE-CARE vision, Wirel Pers Commun, 88, 53, 10.1007/s11277-016-3241-9
Reynolds, 2009, Patient-centered care, Radiol Technol, 81, 133
Berghout, 2015, Healthcare professionals’ views on patient-centered care in hospitals, BMC Health Serv Res, 15, 1
Kontio, 2014, Enterprise resource planning systems in healthcare: a qualitative review, Int J Inf Syst Serv Sect, 6, 36, 10.4018/ijisss.2014040103
Evans, 2001, Vital signs in hospital patients: a systematic review, Int J Nurs Stud, 38, 643, 10.1016/S0020-7489(00)00119-X
Elliott, 2012, Critical care: the eight vital signs of patient monitoring, Br J Nurs, 21, 621, 10.12968/bjon.2012.21.10.621
C.f.C.P.a.N. UK, 2007
Cardona-Morrell, 2015, 533
James, 2010, Vital signs for vital people: an exploratory study into the role of the Healthcare Assistant in recognising, recording and responding to the acutely ill patient in the general ward setting, J Nurs Manage, 18, 548, 10.1111/j.1365-2834.2010.01086.x
Simel, 2012
Cooper, 2006
Jones, 1979, Glasgow coma scale, AJN Am J Nurs, 79, 1551
Fink, 2000
Hersch, 2009, Accuracy and ease of use of a novel electronic urine output monitoring device compared with standard manual urinometer in the intensive care unit, J Crit Care, 24, 10.1016/j.jcrc.2008.12.008
Hourihan, 2011, The Medical Emergency Team: a new strategy to identify and intervene in high-risk patients, Clin Intens Care, 6, 269, 10.3109/tcic.6.6.269.272
Goldhill, 1999, The patient-at-risk team: identifying and managing seriously ill ward patients, Anaesthesia, 54, 853, 10.1046/j.1365-2044.1999.00996.x
Garcea, 2010, Preoperative early warning scores can predict in-hospital mortality and critical care admission following emergency surgery, J Surg Res, 159, 729, 10.1016/j.jss.2008.08.013
Silva, 2016, Medical emergency team: how do we play when we stay? Characterization of met actions at the scene, Scand J Trauma Resusc Emerg Med, 24, 1, 10.1186/s13049-016-0222-7
Pirret, 2015, The effectiveness of a patient at risk team comprised of predominantly ward experienced nurses: a before and after study, Intens Crit Care Nurs, 31, 133, 10.1016/j.iccn.2014.10.005
Buist, 2013, Patient bedside observations: what could be simpler?, BMJ Qual Saf, 22, 699, 10.1136/bmjqs-2013-002143
Subbe, 2001, Validation of a modified Early Warning Score in medical admissions, QJM, 94, 521, 10.1093/qjmed/94.10.521
Stenhouse, 2000, Prospective evaluation of a modified early warning score to aid earlier detection of patients developing critical illness on a general surgical ward, Br J Anaesth, 84, 663, 10.1093/bja/84.5.663
Prytherch, 2010, ViEWS: towards a national early warning score for detecting adult inpatient deterioration, Resuscitation, 81, 932, 10.1016/j.resuscitation.2010.04.014
Duckitt, 2007, Worthing physiological scoring system: derivation and validation of a physiological early-warning system for medical admissions. An observational, population-based single-centre study, Br J Anaesth, 98, 769, 10.1093/bja/aem097
Clifton, 2014, Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors, IEEE J Biomed Health Informatics, 18, 722, 10.1109/JBHI.2013.2293059
International Organization for Standardization, 2005
Tang, 2006, Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption, J Am Med Informatics Assoc, 13, 121, 10.1197/jamia.M2025
Belyaev, 2013, Personal health record storage on privacy preserving green clouds, 2013 9th international conference on collaborative computing: networking, applications and worksharing (Collaboratecom), 448
Roehrs, 2017, Personal health records: a systematic literature review, J Med Internet Res, 19, e13, 10.2196/jmir.5876
International Organization for Standardization, 2012
Triantafyllidis, 2013, A pervasive health system integrating patient monitoring, status logging, and social sharing, IEEE J Biomed Health Informatics, 17, 30, 10.1109/TITB.2012.2227269
Schnall, 2016, A user-centered model for designing consumer mobile health (mHealth) applications (apps), J Biomed Informatics, 60, 243, 10.1016/j.jbi.2016.02.002
Chen, 2013, Design of mobile healthcare service with health records format evaluation, 2013 IEEE 17th international symposium on consumer electronics (ISCE), 257, 10.1109/ISCE.2013.6570215
Kharrazi, 2012, Mobile personal health records: an evaluation of features and functionality, Int J Med Informatics, 81, 579, 10.1016/j.ijmedinf.2012.04.007
Perera, 2014, Context aware computing for the internet of things: a survey, communications surveys & tutorials, IEEE, 16, 414
Gubbi, 2013, Internet of Things (IoT): a vision, architectural elements, and future directions, Future Gen Comput Syst, 28, 1645, 10.1016/j.future.2013.01.010
Mell, 2011
Dastjerdi, 2016, Fog computing: helping the Internet of Things realize its potential, Computer, 49, 112, 10.1109/MC.2016.245
Chennamsetty, 2015, Predictive analytics on electronic health records (EHRS) using Hadoop and Hive, 2015 IEEE international conference on electrical, computer and communication technologies (ICECCT), 1
Hay, 2013, Big data opportunities for global infectious disease surveillance, PLoS Med, 10, e1001413, 10.1371/journal.pmed.1001413
Riazul Islam, 2015, The Internet of Things for Health care: a comprehensive survey, IEEE Access, 3, 678, 10.1109/ACCESS.2015.2437951
Hu, 2013, On the application of the Internet of Things in the field of medical and health care, 2053
Zhang, 2014, Ubiquitous WSN for healthcare: recent advances and future prospects, IEEE Internet Things J, 1, 311, 10.1109/JIOT.2014.2329462
Baig, 2013, Smart health monitoring systems: an overview of design and modeling, J Med Syst, 37, 10.1007/s10916-012-9898-z
Lopez, 2013, Survey of Internet of Things technologies for clinical environments, 1349
Lee, 2007, A comparative study of wireless protocols: bluetooth, UWB, ZigBee, and Wi-Fi, IECON 2007 – 33rd annual conference of the IEEE industrial electronics society, 46, 10.1109/IECON.2007.4460126
Niemelä, 2010, 1
Gomez, 2012, Overview and evaluation of bluetooth low energy: an emerging low-power wireless technology, Sensors, 12, 11734, 10.3390/s120911734
Liu, 2013, Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients, Med Biol Eng Comput, 52, 193, 10.1007/s11517-013-1130-x
Roberts, 2006, Radio frequency identification (RFID), Comput Secur, 25, 18, 10.1016/j.cose.2005.12.003
Coskun, 2013, A survey on near field communication (NFC) technology, Wirel Pers Commun, 71, 2259, 10.1007/s11277-012-0935-5
Catarinucci, 2015, An IoT-aware architecture for smart healthcare systems, IEEE Internet Things J, 2, 515, 10.1109/JIOT.2015.2417684
Mehmood, 2014, Performance evaluation of 6LOWPAN based networks for ubiquitous health monitoring system, 1
Fieler, 2013, Eliminating errors in vital signs documentation, Comput Informatics Nurs, 31, 422, 10.1097/01.NCN.0000432125.61526.27
Aminian, 2013, A hospital healthcare monitoring system using wireless sensor networks, J Health Med Inform, 10.4172/2157-7420.1000121
Jara, 2013, Interconnection framework for mHealth and remote monitoring based on the Internet of Things, IEEE J Sel Areas Commun, 31, 47, 10.1109/JSAC.2013.SUP.0513005
Aung, 2016, Leveraging multi-modal sensing for mobile health: a case review in chronic pain, IEEE J Sel Top Signal Process, 1
Chiuchisan, 2014, 532
Pang, 2013, Design of a terminal solution for integration of in-home health care devices and services towards the Internet-of-Things, Enterprise Inform Syst, 9, 86, 10.1080/17517575.2013.776118
Otero, 2014, A new device to automate the monitoring of critical patients’ urine output, BioMed Res Int, 2014, 1, 10.1155/2014/587593
Andreu-Perez, 2015, Big data for health, IEEE J Biomed Health Informatics, 19, 1193, 10.1109/JBHI.2015.2450362
Huang, 2015, Promises and challenges of big data computing in health sciences, Big Data Res, 2, 2, 10.1016/j.bdr.2015.02.002
Viceconti, 2015, Big data, big knowledge: big data for personalized healthcare, IEEE J Biomed Health Informatics, 19, 1209, 10.1109/JBHI.2015.2406883
Mitchell, 1997, 37
Michalski, 2013
Banaee, 2013, Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges, Sensors, 13, 17472, 10.3390/s131217472
Johnson, 2016, Machine learning and decision support in critical care, Proc IEEE, 104, 444, 10.1109/JPROC.2015.2501978
Lipton, 2015
Che, 2016
Che, 2015
Che, 2015, Deep computational phenotyping, 507
Ravi, 2017, Deep learning for health informatics, IEEE J Biomed Health Informatics, 21, 4, 10.1109/JBHI.2016.2636665
Lehman, 2015, Patient prognosis from vital sign time series: combining convolutional neural networks with a dynamical systems approach, 2015 computing in cardiology conference (CinC), 1069, 10.1109/CIC.2015.7411099
Laplante, 2016, The internet of things in healthcare: potential applications and challenges, IT Profess, 18, 2, 10.1109/MITP.2016.42
Robkin, 2015, Levels of conceptual interoperability model for healthcare framework for safe medical device interoperability, 2015 IEEE symposium on product compliance engineering (ISPCE), 1
Kasparick, 2015, New IEEE 11073 standards for interoperable, networked point-of-care Medical Devices, 1721
He, 2013, Toward ubiquitous healthcare services with a novel efficient cloud platform, IEEE Trans Biomed Eng, 60, 230, 10.1109/TBME.2012.2222404
Coats, 2014, Bridging electronic health record access to the cloud, 2014 47th Hawaii international conference on system sciences (HICSS), 2948, 10.1109/HICSS.2014.367
Coats, 2014, Leveraging the cloud for electronic health record access, Perspect Health Inform Manage, 1
Pasluosta, 2015, An emerging era in the management of Parkinson's disease: wearable technologies and the Internet of Things, IEEE J Biomed Health Informatics, 19, 1873, 10.1109/JBHI.2015.2461555
Soceanu, 2013, Towards interoperability of ehealth system networked components, 2013 19th international conference on control systems and computer science, 147, 10.1109/CSCS.2013.69
Bowles, 2013, Conducting research using the electronic health record across multi-hospital systems: semantic harmonization implications for administrators, J Nurs Admin, 43, 355, 10.1097/NNA.0b013e3182942c3c
Sachdeva, 2012, Semantic interoperability in standardized electronic health record databases, J Data Inform Qual, 3, 10.1145/2166788.2166789
International Organization for Standardization, 2010
openEHR Foundation, 2016
H.L.S. International, 2016
Robert, 2001, The HL7 clinical document architecture, J Am Med Informatics Assoc, 8, 552, 10.1136/jamia.2001.0080552
Benson, 2016, Principles of FHIR, 329
González-Ferrer, 2013, Data integration for clinical decision support based on openehr archetypes and hl7 virtual medical record, 71
Churpek, 2016, Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards, Crit Care Med, 44, 368, 10.1097/CCM.0000000000001571
Kim, 2011, A comparison of intensive care unit mortality prediction models through the use of data mining techniques, Healthc Informatics Res, 17, 232, 10.4258/hir.2011.17.4.232
van der Ploeg, 2014, Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints, BMC Med Res Methodol, 14, 1, 10.1186/1471-2288-14-137
Van Poucke, 2016, Scalable predictive analysis in critically ill patients using a visual open data analysis platform, PLOS ONE, 11, e0145791, 10.1371/journal.pone.0145791
Eskofier, 2016, Recent machine learning advancements in sensor-based mobility analysis, 1