Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning.

ABSTRACT:

Background

As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke.

Objective

The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year.

Methods

Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. Retrospective (N=823,627, calendar year 2013) and prospective (N=680,810, calendar year 2014) cohorts were formed. A machine learning algorithm, XGBoost, was adopted in the process of feature selection and model building. It generated an ensemble of classification trees

SUBMITTER: Ye C 

PROVIDER: S-EPMC5811646 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature