Using graph rewriting to operationalize medical knowledge for the revision of concurrently applied clinical practice guidelines
Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to b...
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Veröffentlicht in: | Artificial intelligence in medicine 2023-06, Vol.140, p.102550-102550, Article 102550 |
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Sprache: | eng |
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Zusammenfassung: | Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to be augmented with secondary medical knowledge coming from a variety of knowledge repositories. The operationalization of this knowledge is key to increasing CPGs’ uptake in clinical practice. In this work, we propose an approach to operationalizing secondary medical knowledge inspired by graph rewriting. We assume that the CPGs can be represented as task network models, and provide an approach for representing and applying codified medical knowledge to a specific patient encounter. We formally define revisions that model and mitigate adverse interactions between CPGs and we use a vocabulary of terms to instantiate these revisions. We demonstrate the application of our approach using synthetic and clinical examples. We conclude by identifying areas for future work with the vision of developing a theory of mitigation that will facilitate the development of comprehensive decision support for the management of multimorbid patients.
•Integrating medical knowledge to revise concurrently applied CIGs using graph rewriting.•Operationalizing medical knowledge using rewriting operations in a clinical context.•Formalized theory for comprehensive decision support addressing the multimorbidity problem. |
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ISSN: | 0933-3657 1873-2860 |
DOI: | 10.1016/j.artmed.2023.102550 |