Ontology-driven hypothesis generation to explain anomalous patient responses to treatment

Laura Moss, Derek Sleeman, Malcolm Sim, Malcolm Booth, Malcolm Daniel, Lyndsay Donaldson, Charlotte Gilhooly, Martin Hughes, John Kinsella

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)309-315
Number of pages7
JournalKnowledge-Based Systems
Issue number4
Early online date3 Dec 2009
Publication statusPublished - May 2010


  • hypothesis generation
  • intensive care unit
  • ontology
  • anomaly resolution
  • discovery


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