Improved performance of machine learning models in predicting length of stay, discharge disposition, and inpatient mortality after total knee arthroplasty using patient-specific variables.

Zalikha, Abdul K; Court, Tannor; Nham, Fong; El-Othmani, Mouhanad M; Shah, Roshan P

Zalikha, Abdul K; Court, Tannor; Nham, Fong; El-Othmani, Mouhanad M; Shah, Roshan P.

Afiliação

Zalikha AK; Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA.
Court T; Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA.
Nham F; Department of Orthopaedic Surgery and Sports Medicine, Detroit Medical Center, Detroit, MI, 48201, USA. nhamfong@gmail.com.
El-Othmani MM; Department of Orthopaedic Surgery, Columbia University Medical Center, New York, NY, 10032, USA.
Shah RP; Department of Orthopaedic Surgery, Columbia University Medical Center, New York, NY, 10032, USA.

Arthroplasty ; 5(1): 31, 2023 Jul 02.

Article em En | MEDLINE | ID: mdl-37393281

Palavras-chave

Artificial intelligence; Length of stay; Machine learning; Postoperative outcomes; Total knee arthroplasty

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Arthroplasty Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

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