Predicting Optimal Hypertension Treatment Pathways Using Recurrent Neural Networks.

ABSTRACT:

Background

In ambulatory care settings, physicians largely rely on clinical guidelines and guideline-based clinical decision support (CDS) systems to make decisions on hypertension treatment. However, current clinical evidence, which is the knowledge base of clinical guidelines, is insufficient to support definitive optimal treatment.

Objective

The goal of this study is to test the feasibility of using deep learning predictive models to identify optimal hypertension treatment pathways for individual patients, based on empirical data available from an electronic health record database.

Materials and methods

This study used data on 245,499 unique patients who were initially diagnosed with essential hypertension and received anti-hypertensive treatment from January 1, 20

SUBMITTER: Ye X 

PROVIDER: S-EPMC10490557 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature