Using modern risk engines and machine learning/artificial intelligence to predict diabetes complications: A focus on the BRAVO model.

Shao, Hui; Shi, Lizheng; Lin, Yilu; Fonseca, Vivian

Shao, Hui; Shi, Lizheng; Lin, Yilu; Fonseca, Vivian.

Afiliação

Shao H; Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, United States of America.
Shi L; Department of Health Policy and Management, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States of America.
Lin Y; Department of Health Policy and Management, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States of America.
Fonseca V; Department of Medicine and Pharmacology, School of Medicine, Tulane University, New Orleans, LA, United States of America; Tulane University Health Sciences Center, 1430 Tulane Avenue - SL 53, New Orleans, LA 70112, United States of America. Electronic address: vfonseca@tulane.edu.

J Diabetes Complications ; 36(11): 108316, 2022 11.

Article em En | MEDLINE | ID: mdl-36201893

Assuntos

Inteligência Artificial; Complicações do Diabetes; Aprendizado de Máquina; Humanos; Fatores de Risco; Ensaios Clínicos como Assunto

Palavras-chave

Cardiovascular events; Diabetes; Diabetes complications; Risk engine; Risk prediction

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Complicações do Diabetes / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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