Reconciliation of multiple guidelines for decision support: a case study on the multidisciplinary management of breast cancer within the DESIREE project.
Creators
- 1. 1) Sorbonne Universités, UPMC Univ Paris 06, INSERM, Université Paris 13, Sorbonne Paris Cité, UMRS 1142, LIMICS, Paris, France. 2) AP-HP, Hôpital Tenon, Département de Santé Publique, Paris, France. 3) APREC, Paris, France
- 2. Sorbonne Universités, UPMC Univ Paris 06, INSERM, Université Paris 13, Sorbonne Paris Cité, UMRS 1142, LIMICS, Paris, France.
- 3. 1) Sorbonne Universités, UPMC Univ Paris 06, INSERM, Université Paris 13, Sorbonne Paris Cité, UMRS 1142, LIMICS, Paris, France 2) eHeatlh and Biomedical Applications, Vicomtech-IK4, Donostia-San Sebastian, Spain 3) Biodonostia, Donostia-San Sebastian, Spain
- 4. 1) eHeatlh and Biomedical Applications, Vicomtech-IK4, Donostia-San Sebastian, Spain. 2) Biodonostia, Donostia-San Sebastian, Spain
- 5. Computer Science Research Institute, Ulster University, Newtownabbey, United Kingdom
- 6. AP-HP, Hôpital Tenon, Service d'Oncologie Médicale, Paris, France
- 7. 1) Sorbonne Universités, UPMC Univ Paris 06, INSERM, Université Paris 13, Sorbonne Paris Cité, UMRS 1142, LIMICS, Paris, France. 2) AP-HP, DRCI, Paris, France.
Description
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.
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