Using statistical text classification to identify health information technology incidents

Journal of the American Medical Informatics Association

Volumn 20, Issue 5, 2013, Pages 980-985

  Chai, Kevin E K ^a   Anthony, Stephen ^b   Coiera, Enrico ^a   Magrabi, Farah ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ANALYTICAL ERROR; ARTICLE; DATA BASE; FEASIBILITY STUDY; FOOD AND DRUG ADMINISTRATION; INCIDENT REPORT; MEDICAL INFORMATICS; RECALL; STATISTICAL ANALYSIS; TRAINING; UNITED STATES; VALIDATION PROCESS;

FDA; HEALTH INFORMATION TECHNOLOGY; HIT; INCIDENTS; MAUDE; TEXT CLASSIFICATION;

DATA MINING; DATABASES, FACTUAL; FEASIBILITY STUDIES; MEDICAL ERRORS; MEDICAL INFORMATICS; UNITED STATES; UNITED STATES FOOD AND DRUG ADMINISTRATION;

EID: 84882801679     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2012-001409     Document Type: Article

References (34)

1. USOffice of the National Coordinator for Health IT, HIT Policy Committee. Adoption/Certification Workgroup meeting 25 February 2010. http://healthit.hhs.gov/ (accessed Apr 2010).
2. US Joint Commission on Accreditation of Healthcare Organizations. 2008. http://jointcommission.org/SentinelEvents/SentinelEventAlert/sea_42.htm (accessed Dec 2008).
3. Ammenwerth E, Schnell-Inderst P, Machan C, et al. The effect of electronic prescribing on medication errors and adverse drug events: a systematic review. J Am Med Inform Assoc 2008;15:585-600.
4. Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004;11:104-12.
5. Coiera E, Westbrook J, Wyatt J. The safety and quality of decision support systems. Methods Inf Med 2006;45(Suppl 1):20-5.
6. Coiera E, Aarts J, Kulikowski C. The dangerous decade. J Am Med Inform Assoc 2012;19:2-5.
7. Bloomrosen M, Starren J, Lorenzi NM, et al. Anticipating and addressing the unintended consequences of health IT and policy: a report from the AMIA 2009 Health Policy Meeting. J Am Med Inform Assoc 2011;18:82-90.
8. Karsh BT, Weinger MB, Abbott PA, et al. Health information technology: fallacies and sober realities. J Am Med Inform Assoc 2010;17:617-23.
9. Runciman WB, Edmonds MJ, Pradhan M. Setting priorities for patient safety. Qual Saf Health Care 2002;11:224-9.
10. Goodman KW, Berner ES, Dente MA, et al. Challenges in ethics, safety, best practices, and oversight regarding HIT vendors, their customers, and patients: a report of an AMIA special task force. J Am Med Inform Assoc 2011;18:77.
11. Magrabi F, Ong MS, Runciman W, et al. Using FDA reports to inform a classification for health information technology safety problems. J Am Med Inform Assoc 2012;19:45-53.
12. The Office of the National Coordinator for Health Information Technology, Health Information Technology Patient Safety Action & Surveillance Plan for Public Comment. 2012. http://www.healthit.gov/sites/default/files/safetyplanhhspubliccomment.pdf (accessed Jan 2013).
13. Magrabi F, Ong MS, Runciman W, et al. An analysis of computer-related patient safety incidents to inform the development of a classification. J Am Med Inform Assoc 2010;17:663-70.
14. Ong MS, Magrabi F, Coiera E. Automated categorisation of clinical incident reports using statistical text classification. Qual Safety Health Care 2010;19:1-7.
15. Ong MS, Magrabi F, Coiera E. Automated identification of extreme-risk events in clinical incident reports. J Am Med Inform Assoc 2012;19:110-18.
16. Savova GK, Olson JE, Murphy SP, et al. Automated discovery of drug treatment patterns for endocrine therapy of breast cancer within an electronic medical record. J Am Med Inform Assoc 2011;19:83-9.
17. Percha B, Nassif H, Lipson J, et al. Automatic classification of mammography reports by BI-RADS breast tissue composition class. J Am Med Inform Assoc 2012;19:913-16.
18. Xu Y, Tsujii J, Eric I, et al. Named entity recognition of follow-up and time information in 20 000 radiology reports. J Am Med Inform Assoc 2012;19:792-99.
19. Wagholikar KB, MacLaughlin KL, Henry MR, et al. Clinical decision support with automated text processing for cervical cancer screening. J Am Med Inform Assoc 2012;19:833-39.
20. Figueroa RL, Zeng-Treitler Q, Ngo LH, et al. Active learning for clinical text classification: is it better than random sampling?. J Am Med Inform Assoc 2012;19:833-39.
21. Seiffert C, Khoshgoftaar TM, Van Hulse J, et al. Mining data with rare events: a case study. IEEE International Conference on Tools with Artificial Intelligence, Patras, Greece, 2007;2:132-39.
22. Weiss GM. Mining with rarity: a unifying framework. SIGKDD Explorations 2004;6:7-19.
23. Gwet K. Inter-rater reliability: dependency on trait prevalence and marginal homogeneity. Statistical Methods Inter-rater Reliability Assessment 2002;2:1-9.
24. Jolliffe IT. Principal component analysis. Berlin: Springer, 1986.
25. Lee S-i, Lee H, Abbeel P, et al. Efficient L1 Regularized Logistic Regression. The 21st AAAI Conference on Artificial Intelligence, Boston, USA 2006.
26. Liu B. Web data mining: exploring hyperlinks, contents, and usage data. Secaucus, USA: Springer Verlag, 2007.
27. Ng A, Jordan M. On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. Adv Neural Inf Process Syst 2002;14:841.
28. Komarek P, Moore A. Fast robust logistic regression for large sparse datasets with binary outputs. Artif Intell Stat 2003;83.
29. Komarek P, Moore AW. Making logistic regression a core data mining tool with tr-irls. Fifth IEEE International Conference on Data Mining, Houston, USA, 2005:4.
30. Hestenes MR, Stiefel E. Methods of conjugate gradients for solving linear systems. J Res Natl Bur Stand 1952;49:409-36.
31. Hsu CW, Chang CC, Lin CJ. A practical guide to support vector classification. 2003. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (accessed 10 Oct 2012).
32. Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning (ICML), Pittsburgh, USA, 2006:233-40.
33. Chu CT, Kim SK, Lin YA, et al. Map-reduce for machine learning on multicore. Adv Neural Inf Process Sys 2007;19:281.
34. Weinberger K, Dasgupta A, Langford J, et al. Feature hashing for large scale multitask learning. Proceedings of the 26th Annual International Conference on Machine Learning (ICML), Montreal, Canada, 2009:1113-220. Chai