Decision Support System for Selection of e-Health Interventions

Autor: Dahbia, Agher, Marc, Fouque, Matteo, Brandi, Karima, Sedki, Rosy, Tsopra, Pierre, Meneton, Sylvie, Despres, Marie-Christine, Jaulent
Přispěvatelé: Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Visiomed Group, Sciences de l’information au service de la médecine personnalisée = Information Sciences to support Personalized Medicine [CRC], 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), Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Studies in Health Technology and Informatics
Studies in Health Technology and Informatics, 2020, 272, pp.326-329. ⟨10.3233/SHTI200561⟩
ISSN: 0926-9630
1879-8365
Popis: International audience; The main goal of this work was to design a decision support system for effective personalized cardiovascular risk prevention: i) to identify behavioral groups associated with clinical risk factors, ii) to provide recommendations associated with the objective to be achieved and iii) to determine the decision-making rules assigning each group to the type of mobile health intervention conveying the most appropriate prevention messages, to help patients to achieve attainable goals. The system is based on an existing data prediction model taking into account specific risky behaviors, clinical risk factors and social status, and it is embedded in a new e-health application. The system is operational. The next step will be the design of a large study to assess improvements in patient adherence to prevention messages through e-health interventions selected by the application.
Databáze: OpenAIRE
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