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

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.

• Type: Conference
• Year: 2009
• Where: SGAI
• Authors: Laura Moss, Derek H. Sleeman, Malcolm Sim, Malcolm Booth, Malcolm Daniel, Lyndsay Donaldson, Charlotte J. Gilhooly, Martin Hughes, John Kinsella
• Keywords: Anomalous Patient Responses, Anomalous Responses, Artificial Intelligence, Clinicians, SGAI 2009