Machine learning prediction of diabetic foot ulcers in the inpatient population.

Stefanopoulos, Stavros; Ayoub, Samar; Qiu, Qiong; Ren, Gang; Osman, Mohamed; Nazzal, Munier; Ahmed, Ayman

Stefanopoulos, Stavros; Ayoub, Samar; Qiu, Qiong; Ren, Gang; Osman, Mohamed; Nazzal, Munier; Ahmed, Ayman.

Afiliação

Stefanopoulos S; Department of Surgery, College of Medicine and Life Sciences, 89021University of Toledo, Toledo, OH, USA.
Ayoub S; Department of Surgery, College of Medicine and Life Sciences, 89021University of Toledo, Toledo, OH, USA.
Qiu Q; Department of Surgery, College of Medicine and Life Sciences, 89021University of Toledo, Toledo, OH, USA.
Ren G; Department of Surgery, College of Medicine and Life Sciences, 89021University of Toledo, Toledo, OH, USA.
Osman M; Department of Surgery, College of Medicine and Life Sciences, 89021University of Toledo, Toledo, OH, USA.
Nazzal M; Department of Surgery, College of Medicine and Life Sciences, 89021University of Toledo, Toledo, OH, USA.
Ahmed A; Department of Surgery, College of Medicine and Life Sciences, 89021University of Toledo, Toledo, OH, USA.

Vascular ; 30(6): 1115-1123, 2022 Dec.

Article em En | MEDLINE | ID: mdl-34461765

Assuntos

Diabetes Mellitus; Pé Diabético; Humanos; Pé Diabético/diagnóstico; Pé Diabético/epidemiologia; Pacientes Internados; Comorbidade; Incidência; Aprendizado de Máquina; Diabetes Mellitus/epidemiologia

Palavras-chave

Diabetic foot ulcer; artificial intelligence; machine learning; wound care

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pé Diabético / Diabetes Mellitus Tipo de estudo: Diagnostic_studies / Incidence_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Vascular Assunto da revista: ANGIOLOGIA / CARDIOLOGIA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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