AutoScore-Ordinal: an interpretable machine learning framework for generating scoring models for ordinal outcomes.

Saffari, Seyed Ehsan; Ning, Yilin; Xie, Feng; Chakraborty, Bibhas; Volovici, Victor; Vaughan, Roger; Ong, Marcus Eng Hock; Liu, Nan

Saffari, Seyed Ehsan; Ning, Yilin; Xie, Feng; Chakraborty, Bibhas; Volovici, Victor; Vaughan, Roger; Ong, Marcus Eng Hock; Liu, Nan.

Afiliação

Saffari SE; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
Ning Y; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
Xie F; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
Chakraborty B; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
Volovici V; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
Vaughan R; Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.
Ong MEH; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore.
Liu N; Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.

BMC Med Res Methodol ; 22(1): 286, 2022 11 04.

Article em En | MEDLINE | ID: mdl-36333672

Assuntos

Assistência ao Convalescente; Alta do Paciente; Humanos; Aprendizado de Máquina; Readmissão do Paciente; Registros Eletrônicos de Saúde; Estudos Retrospectivos

Palavras-chave

Clinical score; Electronic health records; Interpretable machine learning; Medical decision making; Ordinal outcome

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Alta do Paciente / Assistência ao Convalescente Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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