Analyzing Recommendations Interactions in Clinical Guidelines: Impact of action type hierarchies and causation beliefs. Zamborlini, V., da Silveira, M., Pruski, C., ten Teije, A., & van Harmelen, F. Artificial Intelligence in Medicine AIME, pages 317-326. 2015. ![Artificial Intelligence in Medicine AIME [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recom-mendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends previously proposed models by introducing the notions of action type hierarchy and causation beliefs, and provides a systematic analy-sis of relevant interactions in the context of multimorbidity. Finally, the approach is assessed based on a case-study taken from the literature to highlight the added value of the approach.
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