Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
This work addresses early identification of uncontrolled hypertension for healthcare providers, but it is incremental as it applies existing methods to a specific clinical dataset.
The authors developed machine learning models to identify patients at risk of uncontrolled hypertension within three months using electronic health records, achieving an AUROC of 0.719, which outperformed a baseline of 0.634.
Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension management is thus critical. A particular priority is decreasing the incidence of uncontrolled hypertension. Early identification of patients at risk for uncontrolled hypertension would allow targeted use of personalized, proactive treatments. We develop machine learning models (logistic regression and recurrent neural networks) to stratify patients with respect to the risk of exhibiting uncontrolled hypertension within the coming three-month period. We trained and tested models using EHR data from 14,407 and 3,009 patients, respectively. The best model achieved an AUROC of 0.719, outperforming the simple, competitive baseline of relying prediction based on the last BP measure alone (0.634). Perhaps surprisingly, recurrent neural networks did not outperform a simple logistic regression for this task, suggesting that linear models should be included as strong baselines for predictive tasks using EHR