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

ABSTRACT:

Objective

Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory (LSTM) networks, and longitudinal EHRs.

Materials and methods

We compare this approach to the best performing models reported from previous works, particularly XGboost, applied to aggregated features. Our work is based on data from 233 895 adult patients from a large health system in t

SUBMITTER: Datta S 

PROVIDER: S-EPMC9696747 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature