Journal of Biomedical Informatics
Reasoning by analogy in the generation of domain acceptable ontology refinements
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
Knowledge-Based Systems
Argumentation-logic for explaining anomalous patient responses to treatments
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Applying an ontology approach to IT service management for business-IT integration
Knowledge-Based Systems
Decision support for improved service effectiveness using domain aware text mining
Knowledge-Based Systems
Developing attention focus metrics for autonomous hypothesis generation in data mining
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Argumentation-logic for creating and explaining medical hypotheses
Artificial Intelligence in Medicine
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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.