The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.

Zhao, Daren; Zhang, Huiwu; Cao, Qing; Wang, Zhiyi; He, Sizhang; Zhou, Minghua; Zhang, Ruihua

Zhao, Daren; Zhang, Huiwu; Cao, Qing; Wang, Zhiyi; He, Sizhang; Zhou, Minghua; Zhang, Ruihua.

Afiliación

Zhao D; Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China.
Zhang H; Department of Medical Administration, Sichuan Provincial Orthopedics Hospital, Chengdu, Sichuan, P.R. China.
Cao Q; Department of Medical Administration, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, Sichuan, P.R. China.
Wang Z; Department of Medical Administration, Sichuan Cancer Hospital & Institute, Chengdu, Sichuan, P.R. China.
He S; Department of Information and Statistics, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P.R. China.
Zhou M; Department of Medical Administration, Luzhou People's Hospital, Luzhou, Sichuan, P.R. China.
Zhang R; School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, P.R. China.

PLoS One ; 17(2): e0262734, 2022.

Article en En | MEDLINE | ID: mdl-35196309

Asunto(s)

Aprendizaje Profundo; Modelos Estadísticos; Mycobacterium tuberculosis; Estaciones del Año; Tuberculosis/epidemiología; China/epidemiología; Predicción/métodos; Humanos; Incidencia; Prevalencia; Probabilidad; Pronóstico; Salud Pública; Tuberculosis/microbiología

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Estaciones del Año / Tuberculosis / Modelos Estadísticos / Aprendizaje Profundo / Mycobacterium tuberculosis Tipo de estudio: Incidence_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2022 Tipo del documento: Article

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