An application of one-class support vector machines to nosocomial infection detection

Studies in Health Technology and Informatics

Volumn 107, Issue , 2004, Pages 716-720

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

^a GENEVA UNIVERSITY HOSPITALS   (Switzerland)

^b UNIVERSITY OF GENEVA   (Switzerland)

Author keywords

Infection control; Nosocomial infections; One class learning; Prevalence; Support Vector Machines; Surveillance

Indexed keywords

SPACE SURVEILLANCE; SURVEYS;

CLASSIFICATION TASKS; INFECTION CONTROL; NOSOCOMIAL INFECTION; ONE-CLASS LEARNING; ONE-CLASS SUPPORT VECTOR MACHINE; PREVALENCE; RETROSPECTIVE ANALYSIS; SURVEILLANCE SYSTEMS; ONE CLASS-SVM; SUPPORT VECTORS MACHINE; SURVEILLANCE;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CROSS INFECTION; HEALTH SURVEY; HUMAN; INFECTION CONTROL; INFORMATION PROCESSING; PREVALENCE; RETROSPECTIVE STUDY; SWITZERLAND; UNIVERSITY HOSPITAL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CROSS INFECTION; DATA COLLECTION; HOSPITALS, UNIVERSITY; HUMANS; INFECTION CONTROL; POPULATION SURVEILLANCE; PREVALENCE; RETROSPECTIVE STUDIES; SWITZERLAND;

EID: 84887054161     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-60750-949-3-716     Document Type: Article

References (19)

1. French G, Cheng AF, Wong SL, Donnan S. Repeated prev alence surveys for monitoring effectiveness of hospital infection control. Lancet 1983;2:1021-23.
2. Harbarth S, Ruef C, Francioli P, Widmer A, Pittet D, Network S-N. Nosocomial infections in swiss university hospitals: a multicentre survey and review of the published experience. SchweizMed Wochenschr 1999;129:1521-28.
3. Japkowicz N. Learning from Imbalanced Data Sets: A comparison of various Strategies. In: AAAI Workshop on Learning from Imbalanced Data Sets; 2000; Menlo Park, CA: AAAI Press; 2000.
4. Kubat M, Matwin S. Addressing the Curse of Imbalanced Training Sets: One sided selection. In: Kaufmann M, editor. Proceedings of 14th International Conference in Machine Learning; 1997; San Francisco, CA; 1997. p. 179-186.
5. Chawla N, Bowyer K, Hall L, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Oand Research 2002;16:321-357.
6. Cohen G, Hilario M, Sax H, Hugonnet S. Data Imbalance in surveillance of nosocomial infections. In: International Symposium on Medical Data Analysis; 2003; Berlin; 2003.
7. Domingos P. MetaCost: A General Method for Making Classifiers Cost-Sensitive. Knowledge Discovery in Data Mining 1999:155-164.
8. Ali K, Manganaris S, Srikant R. Partial Classification using Association Rules. In: Knowledge Discovery in Databases and Data Mining; 1997. p. 115-118.
9. Manevitz LM, Youssef M. One-class SVMs for document classification. Journal of Machine Learning Research 2001;2.
10. Kowalczyk A, Raskutti B. One Class SVM for yeast regulation prediction. SIGKDD Explorations 2002;4(2).
11. Vapnik V. Statistical Learning Theory: Wiley; 1998.
12. Cortes C, Vapnik V. Support vector networks. Machine Learning 1995;20(3):273-297.
13. Scholkopf B, Williamson RC, Smola AJ, Shawe-Taylor J, Piatt J. Support vector method for novelty detection. In: Adv. in Neural Information Processing Systems 12; 2000: MIT Press; 2000. p. 582-588.
14. Scholkopf B, Piatt J, Shawe-Taylor J, Smola A, Williamson RC. Estimating the support of a high-dimensional distribution. Neural Computation 1999;13:1443-1471.
15. Christianini N, Taylor JS. An introduction to Support Vector Machines; 2000.
16. Centor RM. Signal detectability. Medical Decision Making 1991;11:102-6.
17. Provost F, Fawcett T, Kohavi R. The case against accuracy estimation for comparing induction algorithms. In: Proc. Fifteenth International Conference on Machine Learning (ICML98): Morgan Kaufmann, San Francisco CA; 1998. p. 445-453.
18. Cohen G, Hilario M, Sax H, Hugonnet S. Asymmetrical Margin Approach to Surveillance of Nosocomial Infections Using Support Vector Classification. In: Intelligent Data Analysis in Medicine and Pharmacology; 2003; Cyprus; 2003.
19. Amari S, Wu A. Conformal transformation of kernel functions: A data-dependent way to improve support vector machine classifiers. Neural processing Letter February 2002;15(1):59-67.