  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
