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Using ontologies to facilitate healthcare process mining and analysis.

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Title: Using ontologies to facilitate healthcare process mining and analysis.
Authors: Dwyer, Owen P1 (AUTHOR) owen.dwyer@cs.ox.ac.uk, Chammas, Lara1 (AUTHOR) lara.chammas@st-annes.ox.ac.uk, Sallinger, Emanuel1,2 (AUTHOR) sallinger@dbai.tuwien.ac.at, Davies, Jim1 (AUTHOR) jim.davies@cs.ox.ac.uk
Source: Journal of Intelligent Information Systems. Jun2026, Vol. 64 Issue 3, p989-1009. 21p.
Subjects: Process mining, Ontologies (Information retrieval), Cancer treatment, Treatment effectiveness, Data analysis, Medical terminology
Abstract: Healthcare organisations collect detailed data on the care that they deliver. This data can be used to identify issues, including deviations from care standards and recommendations, and opportunities for improvement; it can be used also to support the development of new technologies and treatments. The volume and complexity of the data means that automated techniques such as process mining are needed to support the extraction and analysis of relevant information. This paper explains how the ontological information held in clinical terminologies can be used to facilitate process extraction and analysis, by connecting and aggregating clinical events through the classification of diagnoses made and treatments performed. The approach is demonstrated through application to data collected on care delivered to patients with cancer in a major hospital. The results are compared with those obtained from benchmark datasets using approaches in which connections and aggregations are proposed and curated by domain experts. This comparison highlights the potential, and the shortcomings, of ontology-based extraction and analysis in healthcare process mining. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Dwyer%2C+Owen+P%22">Dwyer, Owen P</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> owen.dwyer@cs.ox.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Chammas%2C+Lara%22">Chammas, Lara</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lara.chammas@st-annes.ox.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Sallinger%2C+Emanuel%22">Sallinger, Emanuel</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> sallinger@dbai.tuwien.ac.at</i><br /><searchLink fieldCode="AR" term="%22Davies%2C+Jim%22">Davies, Jim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jim.davies@cs.ox.ac.uk</i>
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  Data: <searchLink fieldCode="DE" term="%22Process+mining%22">Process mining</searchLink><br /><searchLink fieldCode="DE" term="%22Ontologies+%28Information+retrieval%29%22">Ontologies (Information retrieval)</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+treatment%22">Cancer treatment</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+effectiveness%22">Treatment effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+terminology%22">Medical terminology</searchLink>
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  Data: Healthcare organisations collect detailed data on the care that they deliver. This data can be used to identify issues, including deviations from care standards and recommendations, and opportunities for improvement; it can be used also to support the development of new technologies and treatments. The volume and complexity of the data means that automated techniques such as process mining are needed to support the extraction and analysis of relevant information. This paper explains how the ontological information held in clinical terminologies can be used to facilitate process extraction and analysis, by connecting and aggregating clinical events through the classification of diagnoses made and treatments performed. The approach is demonstrated through application to data collected on care delivered to patients with cancer in a major hospital. The results are compared with those obtained from benchmark datasets using approaches in which connections and aggregations are proposed and curated by domain experts. This comparison highlights the potential, and the shortcomings, of ontology-based extraction and analysis in healthcare process mining. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Intelligent Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10844-025-00942-8
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        Text: English
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      – SubjectFull: Cancer treatment
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              Text: Jun2026
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              Y: 2026
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