Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis.

Ning, Jingyuan; Sun, Keran; Wang, Xuan; Fan, Xiaoqing; Jia, Keqi; Cui, Jinlei; Ma, Cuiqing

Ning, Jingyuan; Sun, Keran; Wang, Xuan; Fan, Xiaoqing; Jia, Keqi; Cui, Jinlei; Ma, Cuiqing.

Afiliação

Ning J; Department of Immunology, Hebei Medical University, Shijiazhuang, People's Republic of China.
Sun K; Department of Immunology, Hebei Medical University, Shijiazhuang, People's Republic of China.
Wang X; Department of Immunology, Hebei Medical University, Shijiazhuang, People's Republic of China.
Fan X; Department of Laboratory, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.
Jia K; Department of Immunology, Hebei Medical University, Shijiazhuang, People's Republic of China.
Cui J; Department of Pathology, Shijiazhuang People's Hospital, Shijiazhuang, People's Republic of China.
Ma C; Department of Immunology, Hebei Medical University, Shijiazhuang, People's Republic of China.

Mol Med ; 29(1): 37, 2023 03 20.

Article em En | MEDLINE | ID: mdl-36941583

Assuntos

Monócitos; Sepse; Humanos; Teorema de Bayes; Sepse/diagnóstico; Sepse/genética; Diferenciação Celular; Antibacterianos; Aprendizado de Máquina

Palavras-chave

EVL; Machine learning; Prognosis; Sepsis; Single cell

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Monócitos / Sepse Tipo de estudo: Diagnostic_studies / Prognostic_studies / Screening_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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