Analyzing interactions on combining multiple clinical guidelines

Publication date: Available online 11 April 2017
Source:Artificial Intelligence in Medicine
Author(s): Veruska Zamborlini, Marcos da Silveira, Cedric Pruski, Annette ten Teije, Edwin Geleijn, Marike van der Leeden, Martijn Stuiver, Frank van Harmelen
Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations 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 a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity. The approach is evaluated in a case study on rehabilitation of breast cancer patients, developed in collaboration with experts. The results are considered promising to support the experts in this task.



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