[Machine Learning Methods in Assessing the Risks of Target Organ Damage in Masked Hypertension].

Geltser, B I; Shakhgeldyan, K I; Nazarov, D A; Vetrova, O Ð; Kotelnikov, V N; Karpov, R S

Geltser, B I; Shakhgeldyan, K I; Nazarov, D A; Vetrova, O Ð; Kotelnikov, V N; Karpov, R S.

Afiliación

Geltser BI; School of Biomedicine, Far-East Federal University, Vladivostok, Russia.
Shakhgeldyan KI; School of Biomedicine, Far-East Federal University, Vladivostok, Russia, Institute of Informational Technologies, Vladivostok State University of Economics and Service, Vladivostok, Russia.
Nazarov DA; Institute of Automation and Control Processes, Vladivostok, Russia.
Vetrova OÐ; School of Biomedicine, Far-East Federal University, Vladivostok, Russia.
Kotelnikov VN; School of Biomedicine, Far-East Federal University, Vladivostok, Russia.
Karpov RS; Research Institute of Cardiology, Tomsk National Research Medical Center, Tomsk, Russia.

Kardiologiia ; 60(5): 883, 2020 Jun 03.

Article en Ru | MEDLINE | ID: mdl-32515711

Asunto(s)

Hipertensión Enmascarada; Adulto; Presión Sanguínea; Monitoreo Ambulatorio de la Presión Arterial; Femenino; Humanos; Hipertrofia Ventricular Izquierda; Aprendizaje Automático; Masculino; Estudios Retrospectivos

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Hipertensión Enmascarada Tipo de estudio: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: Ru Revista: Kardiologiia Año: 2020 Tipo del documento: Article

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