1. Design of an Ontology-Based Triage System for Patients with Chronic Pain
- Author
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Alexandre Saadi, Alice Rogier, Anita Burgun, Rosy Tsopra, Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Hôpital Européen Georges Pompidou [APHP] (HEGP), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)
- Subjects
MESH: Referral and Consultation ,MESH: Humans ,Clinical Decision Support System ,MESH: Systematized Nomenclature of Medicine ,Ontology ,[SDV]Life Sciences [q-bio] ,Chronic pain ,MESH: Chronic Pain ,[INFO]Computer Science [cs] ,MESH: Triage - Abstract
International audience; Objective: Waiting time for a consultation for chronic pain is a widespread health problem. This paper presents the design of an ontology use to assess patients referred to a consultation for chronic pain. Methods: We designed OntoDol, an ontology of pain domain for patient triage based on priority degrees. Terms were extracted from clinical practice guidelines and mapped to SNOMED-CT concepts through the Python module Owlready2. Selected SNOMED-CT concepts, relationships, and the TIME ontology, were implemented in the ontology using Protégé. Decision rules were implemented with SWRL. We evaluated OntoDol on 5 virtual cases. Results: OntoDol contains 762 classes, 92 object properties and 18 SWRL rules to assign patients to 4 categories of priority. OntoDol was able to assert every case and classify them in the right category of priority. Conclusion: Further works will extend OntoDol to other diseases and assess OntoDol with real world data from the hospital.
- Published
- 2022