Efficient clinical data analysis for prediction of coal workers' pneumoconiosis using machine learning algorithms.

Dong, Hantian; Zhu, Biaokai; Kong, Xiaomei; Zhang, Xinri

Dong, Hantian; Zhu, Biaokai; Kong, Xiaomei; Zhang, Xinri.

Afiliação

Dong H; Department of Geriatric Diseases, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, People's Republic of China.
Zhu B; National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Province Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, People's Republic of China.
Kong X; Network Security Department, Shanxi Police College, Taiyuan, Shanxi, People's Republic of China.
Zhang X; National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Province Key Laboratory of Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, People's Republic of China.

Clin Respir J ; 17(7): 684-693, 2023 Jul.

Article em En | MEDLINE | ID: mdl-37380332

Assuntos

Antracose; Minas de Carvão; Pneumoconiose; Humanos; Poeira/análise; Carvão Mineral

Palavras-chave

coal workers' pneumoconiosis clinical prediction; feature selection; machine learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pneumoconiose / Minas de Carvão / Antracose Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Clin Respir J Ano de publicação: 2023 Tipo de documento: Article

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