A machine-learning approach to predict postprandial hypoglycemia.

Seo, Wonju; Lee, You-Bin; Lee, Seunghyun; Jin, Sang-Man; Park, Sung-Min

Seo, Wonju; Lee, You-Bin; Lee, Seunghyun; Jin, Sang-Man; Park, Sung-Min.

Afiliación

Seo W; Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea.
Lee YB; Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Seoul, 06351, Republic of Korea.
Lee S; Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea.
Jin SM; Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Seoul, 06351, Republic of Korea. smj0919@skku.edu.
Park SM; Department of Creative IT engineering, POSTECH, 77, Cheongam-Ro, Nam-Gu, Pohang, 37673, Republic of Korea. sungminpark@postech.ac.kr.

BMC Med Inform Decis Mak ; 19(1): 210, 2019 11 06.

Article en En | MEDLINE | ID: mdl-31694629

Asunto(s)

Hipoglucemia/diagnóstico; Hipoglucemia/etiología; Aprendizaje Automático; Adulto; Algoritmos; Glucemia; Automonitorización de la Glucosa Sanguínea; Humanos; Hipoglucemiantes/uso terapéutico; Modelos Logísticos; Estudios Retrospectivos; Sensibilidad y Especificidad; Máquina de Vectores de Soporte

Palabras clave

Diabetes; Hypoglycemia; Machine-learning approach; Risk prediction

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Aprendizaje Automático / Hipoglucemia Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Humans Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2019 Tipo del documento: Article

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