Interpretable machine learning models for hospital readmission prediction: a two-step extracted regression tree approach.

Gao, Xiaoquan; Alam, Sabriya; Shi, Pengyi; Dexter, Franklin; Kong, Nan

Gao, Xiaoquan; Alam, Sabriya; Shi, Pengyi; Dexter, Franklin; Kong, Nan.

Gao X; School of Industrial Engineering, Purdue University, West Lafayette, USA.
Alam S; Department of Electrical Engineering and Computer Sciences, UC Berkeley, Berkeley, USA.
Shi P; Krannert School of Management, Purdue University, West Lafayette, USA. shi178@purdue.edu.
Dexter F; Department of Anesthesia, University of Iowa, Iowa, USA.
Kong N; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, USA.

BMC Med Inform Decis Mak ; 23(1): 104, 2023 06 05.

Article en En | MEDLINE | ID: mdl-37277767

Asunto(s)

Aprendizaje Automático; Readmisión del Paciente; Humanos; Factores de Riesgo; Redes Neurales de la Computación; Algoritmos

Palabras clave

Administrative data; Hospital readmission; Interpretable machine learning; Risk factors; Risk prediction

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Readmisión del Paciente / Aprendizaje Automático Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2023 Tipo del documento: Article

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