A Machine Learning Model to Predict Risperidone Active Moiety Concentration Based on Initial Therapeutic Drug Monitoring.

Guo, Wei; Yu, Ze; Gao, Ya; Lan, Xiaoqian; Zang, Yannan; Yu, Peng; Wang, Zeyuan; Sun, Wenzhuo; Hao, Xin; Gao, Fei

Guo, Wei; Yu, Ze; Gao, Ya; Lan, Xiaoqian; Zang, Yannan; Yu, Peng; Wang, Zeyuan; Sun, Wenzhuo; Hao, Xin; Gao, Fei.

Afiliação

Guo W; Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Yu Z; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Gao Y; Beijing Medicinovo Technology Co. Ltd., Beijing, China.
Lan X; Lugouqiao Community Health Service Center, Beijing, China.
Zang Y; Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Yu P; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Wang Z; Beijing Key Laboratory of Mental Disorders, The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Sun W; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Hao X; Beijing Medicinovo Technology Co. Ltd., Beijing, China.
Gao F; School of Computer Science, The University of Sydney, Sydney, NSW, Australia.

Front Psychiatry ; 12: 711868, 2021.

Article em En | MEDLINE | ID: mdl-34867511

Palavras-chave

XGBoost; active moiety; machine learning; prediction model; risperidone

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Psychiatry Ano de publicação: 2021 Tipo de documento: Article

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