Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery

Journal of Biomedical Informatics

Volumn 42, Issue 2, 2009, Pages 356-364

  Taft, L M ^a   Evans, R S ^a,e   Shyu, C R ^a,c   Egger, M J ^a   Chawla, N ^d   Mitchell, J A ^a   Thornton, S N ^a,e   Bray, B ^a   Varner, M ^b  

^a UNIVERSITY OF UTAH HEALTH SCIENCES CENTER   (United States)

^b UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF MISSOURI   (United States)

^d University of Notre Dame   (United States)

^e INTERMOUNTAIN HEALTHCARE   (United States)

Author keywords

Adverse drug events; Data mining; Labor and delivery; Oversampling; Pregnancy

Indexed keywords

ADVERSE DRUG EVENTS; DATA-MINING; LABOR AND DELIVERY; OVERSAMPLING; PREGNANCY;

CUSTOMER SATISFACTION; DECISION TREES; INTENSIVE CARE UNITS; OBSTETRICS; PATTERN RECOGNITION; PREDICTIVE CONTROL SYSTEMS;

ADVECTION;

ALGORITHM; ARTICLE; COMORBIDITY; COMPUTER PREDICTION; DECISION TREE; DELIVERY; DRUG SURVEILLANCE PROGRAM; EMERGENCY WARD; FEMALE; HUMAN; INTENSIVE CARE; INTERNATIONAL CLASSIFICATION OF DISEASES; LABOR; MAJOR CLINICAL STUDY; MEDICATION ERROR; NAIVE BAYES THEOREM; OPERATING ROOM; PATTERN RECOGNITION; PREDICTOR VARIABLE; PRIORITY JOURNAL; RISK ASSESSMENT; RISK FACTOR; SAMPLING; SYNTHETIC MINORITY OVER SAMPLING TECHNIQUE; VALIDATION PROCESS;

ALGORITHMS; ANALYSIS OF VARIANCE; BAYES THEOREM; DATABASES AS TOPIC; DECISION SUPPORT SYSTEMS, CLINICAL; DECISION TREES; DELIVERY, OBSTETRIC; DRUG TOXICITY; FEMALE; HUMANS; LABOR, OBSTETRIC; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; PREGNANCY; REPRODUCIBILITY OF RESULTS; ROC CURVE; STATISTICS, NONPARAMETRIC;

EID: 62349122663     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2008.09.001     Document Type: Article

References (38)

1. IOM. Preventing Medication Errors: Institute of Medicine; 2006.
2. Rothschild J.M., Landrigan C.P., Cronin J.W., Kaushal R., Lockley S.W., Burdick E., et al. The critical care safety study: the incidence and nature of adverse events and serious medical errors in intensive care. Crit Care Med 33 8 (2005) 1694-1700
3. IOM. To Err is Human: Building a Safer Health System; 1999.
4. Weingart S.N., Mc L.W.R., Gibberd R.W., and Harrison B. Epidemiology of medical error. West J Med 172 6 (2000) 390-393
5. Tsai P.S., Chen C.P., and Tsai M.S. Perioperative vasovagal syncope with focus on obstetric anesthesia. Taiwan J Obstet Gynecol 45 3 (2006) 208-214
6. Cesario S.K. Managing the second stage of labor: using evidence to guide practice. Worldviews Evid Based Nurs 1 4 (2004) 230
7. Shimo T., Nishiike S., Masuoka M., Seki S., and Tsuchida H. Intraoperative anaphylactic shock induced by methylergometrine and oxytocin. Masui 55 4 (2006) 447-450
8. Gaiser R.R., McHugh M., Cheek T.G., and Gutsche B.B. Predicting prolonged fetal heart rate deceleration following intrathecal fentanyl/bupivacaine. Int J Obstet Anesth 14 3 (2005) 208-211
9. Bolukbasi D., Sener E.B., Sarihasan B., Kocamanoglu S., and Tur A. Comparison of maternal and neonatal outcomes with epidural bupivacaine plus fentanyl and ropivacaine plus fentanyl for labor analgesia. Int J Obstet Anesth 14 4 (2005) 288-293
10. Caughey A.B., and Bishop J.T. Maternal complications of pregnancy increase beyond 40 weeks of gestation in low-risk women. J Perinatol 13 (2006)
11. Zorman M., Podgorelec V., Kokol P., Peterson M., Sprogar M., and Ojstersek M. Finding the right decision tree's induction strategy for a hard real world problem. Int J Med Inform 63 1-2 (2001) 109-121
12. Weiss G.M. Mining with rarity: a unifying framework. SIGKDD Explor Newsl. 6 1 (2004) 7-19
13. Chawla N. V. LA, Hall L.O., Bowyer K.,. SMOTEBoost: Improving Prediction of Minority Class in Boosting. 7th European Conference of Priciples and Practice of Knowledge Discovery in Databases (PKDD); 2003; Dubrovnik, Croatia; 2003. p. 107-19.
14. Liu F., and Wets G. A neural network method for prediction of proteolytic cleavage sites in neuropeptide precursors. Conf Proc IEEE Eng Med Biol Soc 3 (2005) 2805-2808
15. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 2003 2002:341-78.
16. Chawla N.V., Lazarevic A., Hall L.O., and Bowyer K.W. SMOTEBoost: improving prediction of minority class in boosting (2004), Dubrovnik, Croatia
17. Witten I.H., and Frank E. Data mining: practical machine learning tools and techniques. 2nd ed. (2005), Morgan Kaufman, Amsterdam, Boston, MA
18. Shortliffe E.H., and Cimino J.J. Biomedical informatics: computer applications in health care and biomedicine. 3rd ed. (2006), Springer, New York, NY
19. Hall M.A., and Holmes G. Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowledge Data Eng 15 3 (2003) 1
20. Yasmeen S., Romano P.S., Schembri M.E., Keyzer J.M., and Gilbert W.M. Accuracy of obstetric diagnoses and procedures in hospital discharge data. Am J Obstet Gynecol 194 4 (2006) 992-1001
21. JCAHO. Joint Commission on Accreditation of Healthcare Organizations. [cited 2005; Available from: http://www.jcaho.org/.
22. Utah TUo, Gynecology DoOa. Joint OB/urogyn/gyn research meeting. Utah: Salt Lake City; 2006.
23. Afifi A.A., and Clark V. Multivariate analysis, canonical correlation analysis. Computer-aided multivariate analysis. 2nd ed. (2004), Van Nostrand Reinhold, New York p. 252-70
24. Evans R.S., Pestotnik S.L., Classen D.C., and Burke J.P. Evaluation of a computer-assisted antibiotic-dose monitor. Ann Pharmacother 33 10 (1999) 1026-1031
25. Fernandez G. Data mining using SAS applications (2003), Chapman & Hall/CRC, Boca Raton
26. Tan P.-N., Steinbach M., and Kumar V. Introduction to data mining. 1st ed. (2006), Pearson Addison Wesley, Boston
27. Nitesh V, Chawla AL, Hall Lawrence O, Bowyer Kevin W. SMOTEBoost: improving prediction of the minority class in boosting 2003.
28. Demsar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7 (2006) 1-30
29. Bates D.W., Evans R.S., Murff H., Stetson P.D., Pizziferri L., and Hripcsak G. Detecting adverse events using information technology. J Am Med Inform Assoc 10 2 (2003) 115-128
30. Cannon-Albright L.A., Farnham J.M., Thomas A., and Camp N.J. Identification and study of Utah pseudo-isolate populations-prospects for gene identification. Am J Med Genet 137 3 (2005) 269-275
31. Romano P.S., Yasmeen S., Schembri M.E., Keyzer J.M., and Gilbert W.M. Coding of perineal lacerations and other complications of obstetric care in hospital discharge data. Obstet Gynecol 106 4 (2005) 717-725
32. Geller S.E., Rosenberg D., Cox S., Brown M., Simonson L., and Kilpatrick S. A scoring system identified near-miss maternal morbidity during pregnancy. J Clin Epidemiol 57 7 (2004) 716-720
33. Allen-Brady K., Camp N.J., Ward J.H., and Cannon-Albright L.A. Lobular breast cancer: excess familiality observed in the Utah Population Database. Int J Cancer 117 4 (2005) 655-661
34. Geller S.E., Cox S.M., and Kilpatrick S.J. A descriptive model of preventability in maternal morbidity and mortality. J Perinatol 26 2 (2006) 79-84
35. Geller S.E., Rosenberg D., Cox S.M., and Kilpatrick S. Defining a conceptual framework for near-miss maternal morbidity. J Am Med Womens Assoc 57 3 (2002) 135-139
36. Holden D.A., Quin M., and Holden D.P. Clinical risk management in obstetrics. Curr Opin Obstet Gynecol 16 2 (2004) 137-142
37. Evans R.S., Pestotnik S.L., Classen D.C., Bass S.B., Menlove R.L., Gardner R.M., et al. Development of a computerized adverse drug event monitor. Proc Annu Symp Comput Appl Med Care (1991) 23-27
38. Bates D.W., Cullen D.J., Laird N., Petersen L.A., Small S.D., Servi D., et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA 274 1 (1995) 29-34