Search

Your search keyword '"Saadi, Alexandre"' showing total 5 results
Start Over Author "Saadi, Alexandre"
5 results on '"Saadi, Alexandre"'

2. Design of an ontology-based triage system for painful patients

Author

Saadi, Alexandre, Rogier, Alice, Burgun, Anita, Tsopra, Rosy, 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), 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)), 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), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), 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), and SAADI, Alexandre

Subjects
MESH: Humans, Clinical Decision Support System, [SDV.MHEP.PHY] Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Ontology, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Chronic pain, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, MESH: Referral and Consultation, MESH: Systematized Nomenclature of Medicine, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.MHEP.PHY]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], MESH: Chronic Pain, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, MESH: Triage
Abstract

International audience; Objective: Waiting time for a consultation for chronic pain is a widespread health problem. This paper present 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

4. Design of an Ontology-Based Triage System for Patients with Chronic Pain.

Author

SAADI, Alexandre, ROGIER, Alice, BURGUN, Anita, and TSOPRA, Rosy

Subjects
CHRONIC pain treatment, MEDICAL triage, VISUAL analog scale, CONFERENCES & conventions, MEDICAL protocols, ONTOLOGIES (Information retrieval), SYSTEMATIZED Nomenclature of Medicine, COMPUTED tomography, DATA analysis software, ALGORITHMS
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
View/download PDF

5. Cost effectiveness in a supportive care unit.

Author

Morin, Sophie, primary, Leroy, Pauline, additional, Aubaret, Christophe, additional, Legeay, Karine, additional, Saadi, Alexandre, additional, Brami, Cloé, additional, Gervais, Claire, additional, Herve, Christian, additional, Mamzer, Marie-France, additional, Sauvajot, Clarisse, additional, Guillou, Anne, additional, Garnier, Barbara, additional, Lecuru, Fabrice, additional, Waechter, Lorraine, additional, and Scotte, Florian, additional

Published
2017
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results