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.

Affiliation

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 in En | MEDLINE | ID: mdl-34461765

Subject(s)

Diabetes Mellitus; Diabetic Foot; Humans; Diabetic Foot/diagnosis; Diabetic Foot/epidemiology; Inpatients; Comorbidity; Incidence; Machine Learning; Diabetes Mellitus/epidemiology

Key words

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

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Diabetic Foot / Diabetes Mellitus Type of study: Diagnostic_studies / Incidence_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Vascular Journal subject: ANGIOLOGIA / CARDIOLOGIA Year: 2022 Document type: Article Affiliation country: United States Country of publication: United kingdom

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