Extracting New Temporal Features to Improve the Interpretability of Undiagnosed Type 2 Diabetes Mellitus Prediction Models.

Kocbek, Simon; Kocbek, Primoz; Gosak, Lucija; Fijacko, Nino; Stiglic, Gregor

Kocbek, Simon; Kocbek, Primoz; Gosak, Lucija; Fijacko, Nino; Stiglic, Gregor.

Afiliación

Kocbek S; Institute of Informatics, Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia.
Kocbek P; Faculty of Health Sciences, University of Maribor, 2000 Maribor, Slovenia.
Gosak L; Faculty of Health Sciences, University of Maribor, 2000 Maribor, Slovenia.
Fijacko N; Faculty of Health Sciences, University of Maribor, 2000 Maribor, Slovenia.
Stiglic G; Institute of Informatics, Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia.

J Pers Med ; 12(3)2022 Feb 28.

Article en En | MEDLINE | ID: mdl-35330368

Palabras clave

LogicRegression; diabetes mellitus type 2; interpretability; prediction model

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Pers Med Año: 2022 Tipo del documento: Article País de afiliación: Eslovenia

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