Reconciliation of multiple guidelines for decision support: a case study on the multidisciplinary management of breast cancer within the DESIREE project.

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. PMID: 29854222 [PubMed - indexed for MEDLINE]
Source: AMIA Annual Symposium Proceedings - Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research