Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania.

Fahey, Carolyn A; Wei, Linqing; Njau, Prosper F; Shabani, Siraji; Kwilasa, Sylvester; Maokola, Werner; Packel, Laura; Zheng, Zeyu; Wang, Jingshen; McCoy, Sandra I

Fahey, Carolyn A; Wei, Linqing; Njau, Prosper F; Shabani, Siraji; Kwilasa, Sylvester; Maokola, Werner; Packel, Laura; Zheng, Zeyu; Wang, Jingshen; McCoy, Sandra I.

Afiliação

Fahey CA; Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington, United States of America.
Wei L; Division of Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America.
Njau PF; Ministry of Health, Dodoma, Tanzania.
Shabani S; Ministry of Health, Dodoma, Tanzania.
Kwilasa S; Ministry of Health, Dodoma, Tanzania.
Maokola W; Ministry of Health, Dodoma, Tanzania.
Packel L; Division of Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America.
Zheng Z; Department of Industrial Engineering and Operations Research, University of California, Berkeley, California, United States of America.
Wang J; Division of Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America.
McCoy SI; Division of Epidemiology, School of Public Health, University of California, Berkeley, California, United States of America.

PLOS Glob Public Health ; 2(9): e0000720, 2022.

Article em En | MEDLINE | ID: mdl-36962586

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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