OPTIMISING CLASSIFIERS FOR THE DETECTION OF PHYSIOLOGICAL DETERIORATION IN PATIENT VITAL-SIGN DATA

Sara Khalid, David A. Clifton, Lei Clifton, Lionel Tarassenko

2011

Abstract

Hospital patient outcomes can be improved by the early identification of physiological deterioration. Automatic methods of detecting patient deterioration in vital-sign data typically attempt to identify deviations from assumed “normal” physiological condition. This paper investigates the use of a multi-class approach, in which “abnormal” physiology is modelled explicitly. The success of such a method relies on the accuracy of data annotations provided by clinical experts. We propose an approach to estimate class labels provided by clinicians, and refine those labels such they may be used to optimise a multi-class classifier for identifying patient deterioration. We demonstrate the effectiveness of the proposed methods using a large data-set acquired in a 24-bed step-down unit.

References

  1. Buist, M. D., Jarmolowski, E., Burton, P. R., Bernard, S. A., Waxman, B. P., Anderson, J., 1999: Recognising Clinical Instability in Hospital Patients Before Cardiac Arrest or Unplanned Admission to Intensive Care. Med. J. Australia, Vol. 171 8-9.
  2. NPSA, 2007. National Patient Safety Association: Safer Care for Acutely Ill Patients: Learning from Serious Accidents. Tech. Rep
  3. Tarassenko, L., Hann, A., Patterson, A., Braithwaite, E., Davidson, K., Barber, V., Young, D., 2005: Multiparameter Monitoring for Early Warning of Patient Deterioration. Proc. 3rd IEE Int. Seminar on Medical Applications of Signal Processing, London, 71-6.
  4. Smith, G. B., Prytherch, D. R., Schmidt, P. E., Featherstone, P. I.,2008: Review and Performance Evaluation of Aggregate Weight “Track and Trigger” Systems. J. Resuscitation, Vol. 77, 170-179.
  5. Tarassenko, L., Clifton, D. A., Bannister, P. R., King, S., King, D., 2009: Novelty Detection. In: Staszewski, W. J., Worden, K. (eds), Encyclopaedia of Structural Health Monitoring, Wiley.
  6. Bishop, C. M., 2006.: Pattern Recognition and Machine Learning. Springer-Verlag.
Download


Paper Citation


in Harvard Style

Khalid S., Clifton D., Clifton L. and Tarassenko L. (2011). OPTIMISING CLASSIFIERS FOR THE DETECTION OF PHYSIOLOGICAL DETERIORATION IN PATIENT VITAL-SIGN DATA . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 425-428. DOI: 10.5220/0003138904250428


in Bibtex Style

@conference{biosignals11,
author={Sara Khalid and David A. Clifton and Lei Clifton and Lionel Tarassenko},
title={OPTIMISING CLASSIFIERS FOR THE DETECTION OF PHYSIOLOGICAL DETERIORATION IN PATIENT VITAL-SIGN DATA},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={425-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003138904250428},
isbn={978-989-8425-35-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - OPTIMISING CLASSIFIERS FOR THE DETECTION OF PHYSIOLOGICAL DETERIORATION IN PATIENT VITAL-SIGN DATA
SN - 978-989-8425-35-5
AU - Khalid S.
AU - Clifton D.
AU - Clifton L.
AU - Tarassenko L.
PY - 2011
SP - 425
EP - 428
DO - 10.5220/0003138904250428