Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records.

Tang, Baoyu; Yuan, Yuyu; Yang, Jincui; Qiu, Lirong; Zhang, Shasha; Shi, Jinsheng

Tang, Baoyu; Yuan, Yuyu; Yang, Jincui; Qiu, Lirong; Zhang, Shasha; Shi, Jinsheng.

Afiliación

Tang B; School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China.
Yuan Y; Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing 100876, China.
Yang J; School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China.
Qiu L; Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing 100876, China.
Zhang S; School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China.
Shi J; Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing 100876, China.

Sensors (Basel) ; 22(21)2022 Nov 03.

Article en En | MEDLINE | ID: mdl-36366151

Asunto(s)

Glucemia; Diabetes Mellitus Tipo 1; Humanos; Insulina de Acción Corta; Automonitorización de la Glucosa Sanguínea/métodos; Insulina/uso terapéutico

Palabras clave

blood glucose prediction; deep learning; deep neural network; insulin efficacy prediction

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Glucemia / Diabetes Mellitus Tipo 1 Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

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