Identification of patient deterioration in vital-sign data using one-class support vector machines

2011 Federated Conference on Computer Science and Information Systems, FedCSIS 2011

Volumn , Issue , 2011, Pages 125-131

  Clifton, Lei ^a   Clifton, David A ^a   Watkinson, Peter J ^b   Tarassenko, Lionel ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

novelty detection; one class classification; parameter optimisation; partial AUC; support vector machine

Indexed keywords

NOVELTY DETECTION; ONE-CLASS CLASSIFICATION; PARAMETER OPTIMISATION; PARTIAL AUC; SUPPORT VECTOR;

ALARM SYSTEMS; COMPUTER SCIENCE; DETERIORATION; HOSPITALS; INFORMATION SYSTEMS; PATIENT MONITORING; SUPPORT VECTOR MACHINES;

MEDICAL COMPUTING;

EID: 83155184726     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. National Patient Safety Association, "Safer care for acutely ill patients: Learning from serious accidents," NPSA, Tech. Rep., 2007.
2. National Institute for Clinical Excellence, "Recognition of and response to acute illness in adults in hospital," NICE, Tech. Rep., 2007.
3. L. Tarassenko, D. Clifton, M. Pinsky, M. Hravnak, J. Woods, and P. Watkinson, "Centile-based early warning scores derived from statistical distributions of vital signs," Resuscitation, no. DOI:10.1016/j. resuscitation.2011.03.006, 2011.
4. P. Hayton, L. Tarassenko, B. Scholkopf, and P. Anuzis, "Support vector novelty detection applied to jet engine vibration spectra," in Proc. NIPS, Denver, US, 2000, pp. 946-952.
5. A. Gretton and F. Desobry, "On-line one-class support vector machines. an application to signal segmentation." in Proc. IEEE ICASSP, Hong-Kong, China, 2003.
6. D. R. Hardoon and L. M. Manevitz, "fMRI analysis via one-class machine learning techniques," in Proc. 19th International Joint Conference on Aritifical Intelligence (IJCAI), Edinburgh, UK, 2005, pp. 1604-1605.
7. M. Markou and S. Singh, "Novelty detection: A review - part 2: Neural network based approaches," Signal Processing, vol. 83, no. 12, pp. 2499-2521, 2003.
8. B. Scholkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the support of a high-dimensional distribution,"Neural Computation, vol. 13, no. 7, pp. 1443-1471, 2001.
9. C. Burges, "A tutorial on support vector machines for pattern recognition,"Data Mining and Knowledge Discovery, vol. 2, pp. 121-167, 1998. (Pubitemid 128695475)
10. D. M. J. Tax and R. P. W. Duin, "Data domain description using support vectors," in Proc. ESAN99, Brussels, 1999, pp. 251-256.
11. D. Tax and R. Duin, "Support vector domain description," Pattern Recognition Letters, vol. 20, pp. 1191-1199, 1999.
12. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, 1st ed. Cambridge, UK: Cambridge University Press, 2004.
13. A. R. Webb, Statistical Pattern Recognition, 2nd ed. Chichester, England: John Wiley and Sons Ltd., 2002.
14. A. P. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recognition, vol. 30, no. 7, pp. 1145-1159, 1997.
15. J. A. Hanley and B. J. McNeil, "The meaning and use of the area under the receiver operating characteristic (ROC) curve," Radiology, vol. 143, no. 1, pp. 29-36, 1982.
16. S. H. Park, J. M. Goo, and C. H. Jo, "Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists," Korean Journal of Radiology, vol. 5, no. 1, pp. 11-18, 2004.
17. C. M. Bishop, "Novelty detection and neural network validation,"Proceedings of IEE Conference on Vision and Image Signal Processing, vol. 141, no. 4, pp. 217-222, 1994.
18. B. Scholkopf and A. Smola, Learning with Kernels, 1st ed. Cambridge, USA: MIT Press, 2002.
19. L. Tarassenko, A. Hann, and D. Young, "Integrated monitoring and analysis for early warning of patient deterioration," British Journal of Anaesthesia, vol. 98, no. 1, pp. 149-152, 2007.
20. A. Hann, "Multi-parameter monitoring for early warning of patient deterioration," Ph.D. dissertation, University of Oxford, 2008.
21. C. M. Bishop, Pattern Recognition and Machine Learning. Berlin: Springer-Verlag, 2006.
22. C. Tsien and J. Fackler, "Poor prognosis for existing monitors in the intensive care unit," Critical Care Medicine, vol. 25, no. 4, pp. 614-619, 1997. (Pubitemid 27193973)
23. R. Vert and J. Vert, "Consistency and convergence rates of one-class svms and related algorithms," Journal of Machine Learning Research, vol. 7, pp. 817-854, 2006. (Pubitemid 43727540)