Building a Knowledge-Based Tool for Auto-Assessing the Cardiovascular Risk

Adrien Ugon (1) , Amel Imene Hadj Bouzid , Marie-Christine Jaulent (2, 3) , Madeleine Favre (4) , Catherine Duclos (3) , Emmanuel Jobez , Hector Falcoff (4) , Jean-Baptiste Lamy (3) , Rosy Tsopra (5, 6)

1 ESIEE Paris
2 SPIM - Laboratoire de Santé Publique et Informatique Médicale
3 LIMICS - Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé
4 SFTG - Société de Formation Thérapeutique du Généraliste
5 HEGP - Hôpital Européen Georges Pompidou [APHP]
6 CRC (UMR_S_1138 / U1138) - Centre de Recherche des Cordeliers

The prevention of cardiovascular diseases needs first to quantify the cardiovascular risk. To estimate this risk, French national health authorities provided clinical practice guidelines extending the existing European SCORE, which doesn't include all the cardiovascular risk factors (e.g. diabetes). Hence, French national clinical practice guidelines to quantify the cardiovascular risk is able to deal with more clinical situations than the SCORE. The goal of this paper is to formalize knowledge extracted from these guidelines and implement the rules so that they can be used into an auto-assessing tool of cardiovascular risk. Formalization followed five steps and was conducted under the guidance of medical experts. It resulted into a decision tree fed by eight decision variables. Evaluation of the accuracy of the decision tree showed 80% of agreement with an expert in medical informatics in predicting the cardiovascular risk level for 15 different clinical situations. Discrepancies correspond to the knowledge gaps within Clinical Practice Guidelines. We intend to extend the implementation of the decision tree to a complete tool, for allowing patient to auto-assess their cardiovascular risk. This tool will be integrated into a platform providing recommendations adapted to the calculated level of cardiovascular risk.

Mots clés: Clinical Practice Guidelines; Cardiovascular risk; Formalization

Domaines: Santé publique et épidémiologie; Intelligence artificielle [cs.AI]

Adrien Ugon, Amel Imene Hadj Bouzid, Marie-Christine Jaulent, Madeleine Favre, Catherine Duclos, et al.. Building a Knowledge-Based Tool for Auto-Assessing the Cardiovascular Risk. Studies in Health Technology and Informatics, 2018, 247, pp.735-739. ⟨10.3233/978-1-61499-852-5-735⟩. ⟨hal-01803761⟩