Predicting hypertension onset from longitudinal electronic health records with deep learning.

Datta, Suparno; Morassi Sasso, Ariane; Kiwit, Nina; Bose, Subhronil; Nadkarni, Girish; Miotto, Riccardo; Böttinger, Erwin P

Datta, Suparno; Morassi Sasso, Ariane; Kiwit, Nina; Bose, Subhronil; Nadkarni, Girish; Miotto, Riccardo; Böttinger, Erwin P.

Afiliación

Datta S; Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany.
Morassi Sasso A; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Kiwit N; Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany.
Bose S; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Nadkarni G; Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany.
Miotto R; Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany.
Böttinger EP; Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany.

JAMIA Open ; 5(4): ooac097, 2022 Dec.

Article en En | MEDLINE | ID: mdl-36448021

Palabras clave

deep learning; electronic health records; hypertension; machine learning

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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: JAMIA Open Año: 2022 Tipo del documento: Article País de afiliación: Alemania

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