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Authors: Sara Khalid ; David A. Clifton ; Lei Clifton and Lionel Tarassenko

Affiliation: University of Oxford, United Kingdom

ISBN: 978-989-8425-35-5

ISSN: 2184-4305

Keyword(s): Novelty detection, Multi-class classification, SVM, MLP.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Monitoring and Telemetry ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics ; Sensor Networks ; Soft Computing

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.

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Paper citation in several formats:
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, ISSN 2184-4305, pages 425-428. DOI: 10.5220/0003138904250428

@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},
}

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

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