Reconciliation of Multiple Guidelines for Decision Support: A case study on the multidisciplinary management of breast cancer within the DESIREE project

Brigitte Seroussi, Gilles Guézennec, Jean-Baptiste Lamy, Naiara Muro, Nekane Larburu, Boomadevi Sekar, Coralie Prebet, Jacques Bouaud

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Abstract

Breast cancer is the most common cancer among women. DESIREE is a European project which aims at developing web-based services for the management of primary breast cancer by multidisciplinary breast units (BUs). We describe the guideline-based decision support system (GL-DSS) of the project. Various breast cancer clinical practice guidelines (CPGs) have been selected to be concurrently applied to provide state-of-the-art patient-specific recommendations. The aim is to reconcile CPG recommendations with the objective of complementarity to enlarge the number of clinical situations covered by the GL-DSS. Input and output data exchange with the GL-DSS is performed using FHIR. We used a knowledge model of the domain as an ontology on which relies the reasoning process performed by rules that encode the selected CPGs. Semantic web tools were used, notably the Euler/EYE inference engine, to implement the GL-DSS. "Rainbow boxes" are a synthetic tabular display used to visualize the inferred recommendations.
Original languageEnglish
Title of host publicationAMIA Annual Symposium Proceedings
Pages1527-1536
Publication statusE-pub ahead of print - 8 Nov 2017
EventAMIA Annual Symposium 2017 - Washington DC, United States
Duration: 4 Nov 20178 Nov 2017

Conference

ConferenceAMIA Annual Symposium 2017
Abbreviated titleAMIA
CountryUnited States
Period4/11/178/11/17

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Seroussi, B., Guézennec, G., Lamy, J-B., Muro, N., Larburu, N., Sekar, B., ... Bouaud, J. (2017). Reconciliation of Multiple Guidelines for Decision Support: A case study on the multidisciplinary management of breast cancer within the DESIREE project. In AMIA Annual Symposium Proceedings (pp. 1527-1536)