Configurational Approach to Identify Concept Networks in selected Clinical Safety Incident Classes

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dc.contributor.authorGupta, Jaiprakash
dc.contributor.authorPoon, Simon
dc.date.accessioned2020-01-04T07:49:16Z
dc.date.available2020-01-04T07:49:16Z
dc.date.issued2020-01-07
dc.description.abstractClassifying clinical safety incidents (CSI) in their correct classes depends on the multiple concepts used to describe them. Machine learning based classification case study presented in this paper shows that it fails to identify the underlying complex concepts associations between the CSI classes. Two pairs of classes, each having high and low confused classes (as determined by the classifier), were further investigated by applying the set-theoretic-based logical synthesis methodology. The aim is to identify the relationships between concept networks for selected classes. The concept networks were identified using a set of 117 terms and measures taken included degree-centrality and in-betweenness centrality. In this study, using deterministic configurational approach, it is feasible to draw a meaningful relationship between concepts using the complex medical dataset sourced from the Incident Information Management System. The study is proof of concept that it is possible to identify concept networks and concept configuration rules for CSI classes.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.394
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64136
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBig Data on Healthcare Application
dc.subjectclinical safety incidents
dc.subjectconfigurational analysis
dc.subjectdata mining
dc.titleConfigurational Approach to Identify Concept Networks in selected Clinical Safety Incident Classes
dc.typeConference Paper
dc.type.dcmiText

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