Early prediction of moderate-to-severe condition of inhalation-induced acute respiratory distress syndrome via interpretable machine learning.

Wu, Junwei; Liu, Chao; Xie, Lixin; Li, Xiang; Xiao, Kun; Xie, Guotong; Xie, Fei

Wu, Junwei; Liu, Chao; Xie, Lixin; Li, Xiang; Xiao, Kun; Xie, Guotong; Xie, Fei.

Afiliação

Wu J; Library of Graduate School, Chinese People's Liberation Army General Hospital, Beijing, 100853, China.
Liu C; Ping An Healthcare Technology, Beijing, China.
Xie L; Yidu Cloud Technology Inc, Beijing, China.
Li X; College of Pulmonary and Critical Care Medicine, Chinese People's Liberation Army General Hospital, Beijing, 100853, China.
Xiao K; Ping An Healthcare Technology, Beijing, China.
Xie G; College of Pulmonary and Critical Care Medicine, Chinese People's Liberation Army General Hospital, Beijing, 100853, China.
Xie F; Ping An Healthcare Technology, Beijing, China. xieguotong@pingan.com.cn.

BMC Pulm Med ; 22(1): 193, 2022 May 12.

Article em En | MEDLINE | ID: mdl-35550064

Assuntos

Síndrome do Desconforto Respiratório; Humanos; Aprendizado de Máquina; Oxigênio; Pressão Parcial; Estudos Retrospectivos

Palavras-chave

ARDS (acute respiratory distress syndrome); Critical care; Early prediction; Etiology-specific; Interpretable machine learning

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Síndrome do Desconforto Respiratório Tipo de estudo: Guideline / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google