Authors:
Sara Khalid
;
David A. Clifton
;
Lei Clifton
and
Lionel Tarassenko
Affiliation:
University of Oxford, United Kingdom
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.