Ontology-Driven Hypothesis Generation to Explain Anomalous Patient Responses to Treatment

Moss, L. , Sleeman, D., Sim, M., Booth, M., Daniel, M., Donaldson, L., Gilhooly, C. , Hughes, M. and Kinsella, J. (2010) Ontology-Driven Hypothesis Generation to Explain Anomalous Patient Responses to Treatment. In: 29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2009), Cambridge, UK, 15-17 Dec 2009, pp. 63-76. ISBN 9781848829824 (doi: 10.1007/978-1-84882-983-1_5)

Abstract

Within the medical domain there are clear expectations as to how a patient should respond to treatments administered. When these responses are not observed it can be challenging for clinicians to understand the anomalous responses. The work reported here describes a tool which can detect anomalous patient responses to treatment and further suggest hypotheses to explain the anomaly. In order to develop this tool, we have undertaken a study to determine how Intensive Care Unit (ICU) clinicians identify anomalous patient responses; we then asked further clinicians to provide potential explanations for such anomalies. The high level reasoning deployed by the clinicians has been captured and generalised to form the procedural component of the ontology-driven tool. An evaluation has shown that the tool successfully reproduced the clinician’s hypotheses in the majority of cases. Finally, the paper concludes by describing planned extensions to this work.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sim, Malcolm and Kinsella, Professor John and Moss, Dr Laura and Booth, Dr Malcolm and Gilhooly, Dr Charlotte and Daniel, Malcolm and Hughes, Dr Martin
• Authors: Moss, L., Sleeman, D., Sim, M., Booth, M., Daniel, M., Donaldson, L., Gilhooly, C., Hughes, M., and Kinsella, J.
• College/School: College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
• ISBN: 9781848829824