Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study.

Dave, Darpit; Erraguntla, Madhav; Lawley, Mark; DeSalvo, Daniel; Haridas, Balakrishna; McKay, Siripoom; Koh, Chester

Dave, Darpit; Erraguntla, Madhav; Lawley, Mark; DeSalvo, Daniel; Haridas, Balakrishna; McKay, Siripoom; Koh, Chester.

Afiliación

Dave D; Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.
Erraguntla M; Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.
Lawley M; Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.
DeSalvo D; Department of Pediatrics, Baylor College of Medicine / Texas Children's Hospital, Houston, TX, United States.
Haridas B; Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States.
McKay S; Department of Pediatrics, Baylor College of Medicine / Texas Children's Hospital, Houston, TX, United States.
Koh C; Division of Pediatric Urology, Texas Children's Hospital, Houston, TX, United States.

JMIR Diabetes ; 6(2): e26909, 2021 Apr 29.

Article en En | MEDLINE | ID: mdl-33913816

Palabras clave

continuous glucose monitoring; diabetes; false alert rate; glucose monitoring; hypoglycemia; machine learning; model generalizability; quantile regression forests; sustained hypoglycemia

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: JMIR Diabetes Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Canadá

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