Applied Clinical Informatics, Inhaltsverzeichnis Appl Clin Inform 2017; 08(01): 35-46DOI: 10.4338/ACI-2016-09-CR-0148 Case Report Schattauer GmbH Using Active Learning to Identify Health Information Technology Related Patient Safety Events Allan Fong 1 MedStar Institute for Innovation –National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA , Jessica L. Howe 1 MedStar Institute for Innovation –National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA , Katharine T. Adams 1 MedStar Institute for Innovation –National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA , Raj M. Ratwani 1 MedStar Institute for Innovation –National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA 2 Georgetown University Medical Center, 3800 Reservoir Rd NW, Washington, DC 20007 › Institutsangaben Artikel empfehlen Abstract Volltext als PDF herunterladen Keywords KeywordsPatient safety - health information technology - active learning - human-in-the-loop - machine learning - patient safety event reports Referenzen References 1 Charles D, Gabriel M, Searcy T, Carolina N, Carolina S. Adoption of Electronic Health Record Systems among U.. S. Non –Federal Acute Care Hospitals: 2008–2014. 2015 2 Karsh BT, Weinger MB, Abbott PA, Wears RL. Health information technology: fallacies and sober realities. J Am Med Informatics Assoc 2010; 17 (Suppl. 06) 617-623. Available from: http://www.ncbi.nlm.nih.gov pubmed/20962121 3 Fong A, Ratwani RM. An Evaluation of Patient Safety Event Report Categories Using Unsupervised Topic Modeling. Methods Inf Med 2015; 54 (Suppl. 04) 338-345. 4 Magrabi F, Ong M-S, Runciman W, Coiera E. Using FDA reports to inform a classification for health information technology safety problems. J Am Med Informatics Assoc 2012; 19 (Suppl. 01) 45-53. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21903979 5 Borycki EM, Kushniruka W. Towards an integrative cognitive-socio-technical approach in health informatics: analyzing technology-induced error involving health information systems to improve patient safety. Open Med Inform J 2010; 4: 181-187. 6 Walker JM, Hassol A, Bradshaw B, Rezaee ME. Health IT Hazard Manager Beta-Test.. Rockville, MD: 2012. Available from: https://healthit.ahrq.gov/sites/default/files/docs/citation/HealthITHazardManagerFinalReport.pdf 7 Meeks DW, Takian A, Sittig DF, Singh H, Barber N. Exploring the sociotechnical intersection of patient safety and electronic health record implementation. J Am Med Inform Assoc 2014; 21 e1 e28-e34. Available from: http://dx.doi.org/10.1136/amiajnl-2013–001762\nhttp://www.ncbi.nlm.nih.gov pubmed/24052536 8 Walker JM, Hassol A, Bradshaw B, Rezaee ME. Health IT Hazard Manager Beta-Test.. Rockville, MD: 2012 9 Chai KEK, Anthony S, Coiera E, Magrabi F. Using statistical text classification to identify health information technology incidents. J Am Med Informatics Assoc 2013; 20 (Suppl. 05) 1-6. Available from: http://www.ncbi. nlm.nih.gov/pubmed/23666777 10 Singh H, Sittig DF. Measuring and improving patient safety through health information technology: The Health IT Safety Framework. BMJ Qual Saf 2015; (0) 1-7 11 Amato MG, Salazar A, Hickman TT, Quist AJL, Volk LA, Wright A, McEvoy D, Galanter WL, Koppel R, Loudin B, Adelman J, McGreevey JD, Smith DH, Bates DW, Schiff GD. Computerized prescriber order entry –related patient safety reports: analysis of 2522 medication errors. J Am Med Informatics Assoc 2016; 0: 1-6. 12 Settles B. Active Learning.. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool: 2012 13 Settles B. Active Learning Literature Survey.. Madison (WI): University of Wisconsin-Madison. 2010 Jan 16. Computer Sciences Technical Report 1648. 14 Kholghi M, Sitbon L, Zuccon G, Nguyen A. Active learning: a step towards automating medical concept extraction. J Am Med Informatics Assoc 2015; 0: 1-9. Available from: http://jamia.oxfordjournals.org/lookup/doi/10.1093/jamia/ocv069 15 Zhang H, Huang M-L, Zhu X-Y. A Unified Active Learning Framework for Biomedical Relation Extraction. J Comput Sci Technol 2012; 27 (Suppl. 06) 1302-1313. 16 Boström H, Dalianis H. De-identifying health records by means of active learning. In: Recall (micro).. 2012: 90-97. 17 Clancy S, Bayer S, Kozierok R. Active Learning with a Human In The Loop.. Bedford, MA: 2012 18 Settles B, Craven M, Friedland L. Active Learning with Real Annotation Costs.. Proceedings of the NIPS Workshop on Cost-Sensitive Learning 2008. 19 Ong M-S, Magrabi F, Coiera E. Automated categorisation of clinical incident reports using statistical text classification. Qual Saf Health Care 2010; 19 (Suppl. 06) e55. Available from: http://www.ncbi.nlm.nih.gov pubmed/20724392 20 Chai KEK, Anthony S, Coiera E, Magrabi F. Using statistical text classification to identify health information technology incidents. J Am Med Inform Assoc 2013; 20 (Suppl. 05) 980-985. Available from: http://jamia.bmj.com/cgi/doi/10.1136/amiajnl-2012–001409 21 Chang C, Lin C. LIBSVM: A Library for Support Vector Machines. ACM Trans Intell Syst Technol 2011; 2 (Suppl. 03) 27. 22 Lewis DD, Catlett J. Heterogeneous Uncertainty Sampling for Supervised Learning.. In: Machine Learning: Proceedings of the Eleventh International Conference. 1994: 148-156. 23 Manning CD, Raghavan P, Schütze H. Introduction to Information Retrieval.. Cambridge University Press: 2008 24 Donmez P, Carbonell JG. Proactive Learning: Cost-Sensitive Active Learning with Multiple Imperfect Oracles.. In: Proceedings of the 17th ACM conference on Information and knowledge management. ACM 2008: 619-628. 25 Meng X, Bradley J, Yavuz B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai D, Amde M, Owen S, Xin D, Xin R, Franklin M, Zadeh R, Zaharia M, Talwalkar A. MLlib: Machine Learning in Apache Spark.. In: CoRR 2015. 26 Kraska T, Talwalkar A, Duchi JC, Griffith R, Franklin MJ, Jordan MI. MLbase: A Distributed Machine-learning System.. In: CIDR 2013.
