A novel method to derive personalized minimum viable recommendations for type 2 diabetes prevention based on counterfactual explanations.

Lenatti, Marta; Carlevaro, Alberto; Guergachi, Aziz; Keshavjee, Karim; Mongelli, Maurizio; Paglialonga, Alessia

Lenatti, Marta; Carlevaro, Alberto; Guergachi, Aziz; Keshavjee, Karim; Mongelli, Maurizio; Paglialonga, Alessia.

Afiliación

Lenatti M; Institute of Electronics, Information Engineering and Telecommunications (IEIIT), National Research Council of Italy (CNR), Rome, Italy.
Carlevaro A; Institute of Electronics, Information Engineering and Telecommunications (IEIIT), National Research Council of Italy (CNR), Rome, Italy.
Guergachi A; Department of Electrical, Electronics and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy.
Keshavjee K; Ted Rogers School of Management, Toronto Metropolitan University, Toronto, Canada.
Mongelli M; Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, Canada.
Paglialonga A; Department of Mathematics and Statistics, York University, Toronto, Canada.

PLoS One ; 17(11): e0272825, 2022.

Article en En | MEDLINE | ID: mdl-36395096

Asunto(s)

Diabetes Mellitus Tipo 2; Femenino; Humanos; Diabetes Mellitus Tipo 2/prevención & control; Inteligencia Artificial; Biomarcadores; Índice de Masa Corporal; Registros Electrónicos de Salud

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diabetes Mellitus Tipo 2 Tipo de estudio: Guideline / Prognostic_studies / Qualitative_research Límite: Female / Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Estados Unidos

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