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: Chapter in Book/Report/Conference proceedingChapter

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.
Original languageEnglish
Title of host publicationResearch and Development in Intelligent Systems XXVI
Subtitle of host publicationIncorporating Applications and Innovations in Intelligent Systems XVII
EditorsMax Bramer, Richard Ellis, Miltos Petridis
Place of PublicationLondon, United Kingdom
PublisherSpringer-Verlag
Pages63-76
Number of pages14
ISBN (Electronic)978-1848829831
ISBN (Print)1848829825, 978-1848829824
DOIs
Publication statusPublished - 19 Nov 2009
Event29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2009) - Cambridge, United Kingdom
Duration: 15 Dec 200917 Dec 2009

Conference

Conference29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2009)
CountryUnited Kingdom
CityCambridge
Period15/12/0917/12/09

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Intensive Care Units
Therapeutics

Cite this

Moss, L., Sleeman, D., Sim, M., Booth, M., Daniel, M., Donaldson, L., ... Kinsella, J. (2009). Ontology-driven hypothesis generation to explain anomalous patient responses to treatment. In M. Bramer, R. Ellis, & M. Petridis (Eds.), Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII (pp. 63-76). London, United Kingdom: Springer-Verlag. https://doi.org/10.1007/978-1-84882-983-1_5

Ontology-driven hypothesis generation to explain anomalous patient responses to treatment. / Moss, Laura; Sleeman, Derek; Sim, Malcolm; Booth, Malcolm; Daniel, Malcolm; Donaldson, Lyndsay; Gilhooly, Charlotte; Hughes, Martin; Kinsella, John.

Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII. ed. / Max Bramer; Richard Ellis; Miltos Petridis. London, United Kingdom : Springer-Verlag, 2009. p. 63-76.

Research output: Chapter in Book/Report/Conference proceedingChapter

Moss, L, Sleeman, D, Sim, M, Booth, M, Daniel, M, Donaldson, L, Gilhooly, C, Hughes, M & Kinsella, J 2009, Ontology-driven hypothesis generation to explain anomalous patient responses to treatment. in M Bramer, R Ellis & M Petridis (eds), Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII. Springer-Verlag, London, United Kingdom, pp. 63-76, 29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (AI-2009), Cambridge, United Kingdom, 15/12/09. https://doi.org/10.1007/978-1-84882-983-1_5
Moss L, Sleeman D, Sim M, Booth M, Daniel M, Donaldson L et al. Ontology-driven hypothesis generation to explain anomalous patient responses to treatment. In Bramer M, Ellis R, Petridis M, editors, Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII. London, United Kingdom: Springer-Verlag. 2009. p. 63-76 https://doi.org/10.1007/978-1-84882-983-1_5
Moss, Laura ; Sleeman, Derek ; Sim, Malcolm ; Booth, Malcolm ; Daniel, Malcolm ; Donaldson, Lyndsay ; Gilhooly, Charlotte ; Hughes, Martin ; Kinsella, John. / Ontology-driven hypothesis generation to explain anomalous patient responses to treatment. Research and Development in Intelligent Systems XXVI: Incorporating Applications and Innovations in Intelligent Systems XVII. editor / Max Bramer ; Richard Ellis ; Miltos Petridis. London, United Kingdom : Springer-Verlag, 2009. pp. 63-76
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