Identifying Experts Reasoning in Antibiotic Treatment with Preference Learning

Karima Sedki (1) , Chaymae Lakrafli (1) , Jean-Baptiste Lamy (1) , Rosy Tsopra (2, 3)

1 LIMICS - Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé
2 CRC (UMR_S_1138 / U1138) - Centre de Recherche des Cordeliers
3 HEGP - Hôpital Européen Georges Pompidou [APHP]

The aim of this paper is to propose an approach based on preference learning for building a model that represents the closest possible experts reasoning to provide recommendations. We apply this method to antibiotherapy in primary care. The preference model was learned from the recommendations and from a database describing the domain.

Mots clés: Clinical Practice Guidelines; Experts reasoning; Preferences learning

Domaines: Ingénierie biomédicale

Karima Sedki, Chaymae Lakrafli, Jean-Baptiste Lamy, Rosy Tsopra. Identifying Experts Reasoning in Antibiotic Treatment with Preference Learning. Studies in Health Technology and Informatics, 2020, 270, pp.1313-1314. ⟨10.3233/SHTI200418⟩. ⟨hal-02887395⟩