Forecasts of cardiac and respiratory mortality in Tehran, Iran, using ARIMAX and CNN-LSTM models.

Mahmudimanesh, Marzieh; Mirzaee, Moghaddameh; Dehghan, Azizallah; Bahrampour, Abbas

Mahmudimanesh, Marzieh; Mirzaee, Moghaddameh; Dehghan, Azizallah; Bahrampour, Abbas.

Afiliación

Mahmudimanesh M; Department of Biostatistics and Epidemiology, Kerman University of Medical Sciences, Kerman, Iran.
Mirzaee M; Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
Dehghan A; Noncommunicable Disease Research Center, Fasa University of Medical Sciences, Fasa, Iran.
Bahrampour A; Department of Biostatistics and Epidemiology, Faculty of Health, Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran. a.bahrampour@griffith.edu.au.

Environ Sci Pollut Res Int ; 29(19): 28469-28479, 2022 Apr.

Article en En | MEDLINE | ID: mdl-34993813

Asunto(s)

Redes Neurales de la Computación; Enfermedades Respiratorias; Predicción; Humanos; Irán; Factores de Tiempo

Palabras clave

Air pollution; Cardiovascular disease; Deep learning; Machine learning; Prediction; Time series Regression

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Enfermedades Respiratorias / Redes Neurales de la Computación Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: Environ Sci Pollut Res Int Asunto de la revista: SAUDE AMBIENTAL / TOXICOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: Irán

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