Format

Send to

Choose Destination
AMIA Annu Symp Proc. 2018 Apr 16;2017:1527-1536. eCollection 2017.

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

Author information

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.
4
eHeatlh and Biomedical Applications, Vicomtech-IK4, Donostia-San Sebastian, Spain.
5
Biodonostia, Donostia-San Sebastian, Spain.
6
Computer Science Research Institute, Ulster University, Newtownabbey, United Kingdom.
7
AP-HP, Hôpital Tenon, Service d'Oncologie Médicale, Paris, France.
8
AP-HP, DRCI, Paris, France.

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.

PMID:
29854222
PMCID:
PMC5977621
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center