Learning from imbalanced data in surveillance of nosocomial infection

Artificial Intelligence in Medicine

Volumn 37, Issue 1, 2006, Pages 7-18

  Cohen, Gilles ^a   Hilario, Mélanie ^b   Sax, Hugo ^a   Hugonnet, Stéphane ^a   Geissbuhler, Antoine ^a  

^a GENEVA UNIVERSITY HOSPITALS   (Switzerland)

^b UNIVERSITY OF GENEVA   (Switzerland)

Author keywords

Data imbalance; Machine learning; Nosocomial infection; Support vector machines

Indexed keywords

ALGORITHMS; DATA ACQUISITION; HOSPITALS; PATIENT MONITORING; RANDOM PROCESSES; VECTORS;

DATA IMBALANCE; NOSOCOMIAL INFECTION; SUPPORT VECTOR MACHINES;

LEARNING SYSTEMS;

ALGORITHM; ARTICLE; AUTOMATION; EPIDEMIOLOGICAL DATA; HEALTH CARE COST; HEALTH SURVEY; HOSPITAL INFECTION; INFECTION RATE; INFECTION RISK; INFORMATION PROCESSING; INFORMATION PROCESSING DEVICE; MATHEMATICAL COMPUTING; MEDICAL INFORMATICS; MONITORING; PATIENT IDENTIFICATION; PRIORITY JOURNAL; REPRODUCIBILITY; RETROSPECTIVE STUDY; SAMPLING; STATISTICAL SIGNIFICANCE; SWITZERLAND; UNIVERSITY HOSPITAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; CROSS INFECTION; CROSS-SECTIONAL STUDIES; HOSPITALS, UNIVERSITY; HUMANS; INFECTION CONTROL; MODELS, STATISTICAL; POPULATION SURVEILLANCE; RETROSPECTIVE STUDIES; ROC CURVE; SWITZERLAND;

EID: 33646107181     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2005.03.002     Document Type: Article

References (31)

1. French G.G., Cheng A.F., Wong S.L., and Donnan S. Repeated prevalence surveys for monitoring effectiveness of hospital infection control. Lancet 2 (1983) 1021-1023
2. Garner J.S., Jarvis W.R., Emori T.G., Horan T.C., and Huges J.M. CDC definitions for nosocomial infections. Am J Infect Control 16 3 (1988) 128-140
3. Kelsey M.C., Emmerson A.M., and Enstone J.E. The second national prevalence survey of infection in hospitals: methodology. J Hosp Infect 30 1 (1995) 7-29
4. Trick W.E., Zagorski B.M., and Tokars J.I. Computer algorithms to detect bloodstream infections. Emerg Infect Dis 10 9 (2004) 1612-1620
5. Brossette S.E., Sprague A.P., Hardin J.M., Waites K.B., Jones W.T., and Moser S.A. Association rules and data mining in hospital infection control and public health surveillance. J Am Med Inform Assoc 5 4 (1998) 373-391
6. Moser S.A., Jones W.T., and Brossette S.E. Application of data mining to intensive care unit microbiologic data. Emerg Infect Dis 5 3 (1999) 454-457
7. Brossette S.E., Sprague A.P., Jones W.T., and Moser S.A. A data mining system for infection control surveillance. Meth Inf Med 39 4 (2000) 303-310
8. Ma L., Tsui F.C., Hogan W.R., Wagner M.M., and Ma H. A framework for infection control surveillance using association rules. AMIA Annual Symposium (2003), AMIA, Washington 410-414
9. Lamma E., Manservigi M., Mello P., Riguzzi F., Serra R., and Storari S. A system for monitoring nosocomial infections. In: Brause R.W., and Hanisch Ernst. (Eds). ISMDA, LNCS (2000), Springer, Franckfurt, Germany 282-292
10. Harbarth S., Ruef Ch., Francioli P., Widmer A., Pittet D., and Swiss-Noso Network. Nosocomial infections in Swiss university hospitals: a multi-centre survey and review of the published experience. Schweiz Med Wochenschr 129 (1999) 1521-1528
11. Perner P. Data mining on multimedia data (2002), Springer Verlag, Berlin, Heidelberg
12. Japkowicz N. The class imbalance problem: a systematic study. Intell Data Anal J 6 5 (2002) 429-449
13. Chawla N., Bowyer K., Hall L., and Kegelmeyer W.P. SMOTE: Synthetic Minority Over-sampling TEchnique. J Artif Intell Res (JAIR) 16 (2002) 321-357
14. Kubat M., and Matwin S. Addressing the curse of imbalanced data sets: one-sided sampling. In: Fisher D.H. (Ed). Proceedings of the 14th International Conference on Machine Learning (1997), Morgan Kaufmann, San Francisco, CA, USA 179-186
15. Domingos P. Metacost: a general method for making classifiers cost-sensitive. In: Chaudhuri S., and Madigan D. (Eds). Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (1999), ACM Press, New York, NY, USA 155-164
16. Ali K., Manganaris S., and Srikant R. Partial classification using association rules. In: Heckerman David., Mannila Heikki., and Pregibon Daryl. (Eds). Proceedings of the Third International Conference on Knowledge Discovery in Databases and Data Mining (1997), AAAI Press, Menlo Park, California 115-118
17. Vapnik V. Statistical learning theory (1998), John Wiley & Sons, New York
18. Cortes C., and Vapnik V. Support vector networks. Mach Learn 20 3 (1995) 273-297
19. Fletcher R. Practical methods of optimization. 2nd ed (1987), John Wiley & Sons, New York
20. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 2 (1998) 121-167
21. Cristianini N., and Taylor J.S. An introduction to support vector machines (2000), Cambridge University Press, Cambridge, UK
22. Karakoulas G., and Shawe-Taylor J. Optimizing classifiers for imbalanced training sets. In: Kearns M.J., Solla S.A., and Cohn D.A. (Eds). Advances in neural information processing systems (NIPS-99) (1999), The MIT Press, Cambridge, MA 253-259
23. Veropoulos K., Cristianini N., and Campbell C. Controlling the sensitivity of support vector machines. In: Thomas Dean (Ed). Proceedings of the International Joint Conference on Artificial Intelligence (1999), Morgan Kaufmann, San Francisco, CA, USA 55-60
24. Morik K., Brockhausen P., and Joachims T. Combining statistical learning with a knowledge-based approach - a case study in intensive care monitoring. In: Bratko Ivan., and Dzeroski Saso. (Eds). Proceedings of the Sixteenth International Conference on Machine Learning (ICML99) (1999), Morgan Kaufmann, San Francisco, CA, USA 268-277
25. Quinlan J.R. C4.5: programs for machine learning (1993), Morgan Kaufmann, San Mateo, CA
26. Duda R., Hart P., and Stork D. Pattern classification (2000), John Wiley & Sons, New York
27. Freund Y., and Schapire R.E. Experiments with a new boosting algorithm. In: Saitta Lorenza. (Ed). Proceedings of the 13th International Conference on Machine Learning (1996), Morgan Kaufmann, San Francisco, CA, USA 148-156
28. Centor R.M. Signal detectability: the use of ROC curves and their analyses. Med Decis Making 11 (1991) 102-106
29. Provost F., Fawcett T., and Kohavi R. The case against accuracy estimation for comparing induction algorithms. In: Shavlik Jude. (Ed). Proceedings of the 15th International Conference on Machine Learning (ICML98) (1998), Morgan Kaufmann, Madison, WI, USA 445-453
30. Amari S., and Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12 6 (1999) 783-789
31. Wu S., and Amari S. Conformal transformation of kernel functions: a data-dependent way to improve support vector machine classifiers. Neural Process Lett 15 1 (2002) 59-67