Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?

Sinski, Maciej; Berka, Petr; Lewandowski, Jacek; Sobieraj, Piotr; Piechocki, Kacper; Paleczny, Bartlomiej; Siennicka, Agnieszka

Sinski, Maciej; Berka, Petr; Lewandowski, Jacek; Sobieraj, Piotr; Piechocki, Kacper; Paleczny, Bartlomiej; Siennicka, Agnieszka.

Afiliação

Sinski M; Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland.
Berka P; Department of Information and Knowledge Engineering, Faculty of Informatics and Statistics, Prague University of Economics and Business, W. Churchill Sq. 4, 120 00 Prague, Czech Republic.
Lewandowski J; Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland.
Sobieraj P; Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland.
Piechocki K; Department of Internal Medicine, Hypertension and Vascular Diseases, Medical University of Warsaw, Banacha 1a, 02-097 Warsaw, Poland.
Paleczny B; Department of Physiology and Pathophysiology, Wroclaw Medical University, Chalubinskiego 10, 50-368 Wroclaw, Poland.
Siennicka A; Department of Physiology and Pathophysiology, Wroclaw Medical University, Chalubinskiego 10, 50-368 Wroclaw, Poland.

J Clin Med ; 11(24)2022 Dec 15.

Article em En | MEDLINE | ID: mdl-36556072

Palavras-chave

SPRINT trial; cardiovascular risk; diastolic blood pressure; machine learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Clin Med Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Polônia

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