Predicting changes in hypertension control using electronic health records from a chronic disease management program
- Jimeng Sun1,
- Candace D McNaughton2,
- Ping Zhang1,
- Adam Perer1,
- Aris Gkoulalas-Divanis3,
- Joshua C Denny4,5,
- Jacqueline Kirby6,
- Thomas Lasko4,
- Alexander Saip6,
- Bradley A Malin4,7
- 1Healthcare Analytics, IBM TJ Watson Research Center, Yorktown Heights, New York, USA
- 2Department of Emergency Medicine, School of Medicine, Vanderbilt University, Nashville, Tennessee, USA
- 3IBM Research-Ireland, Dublin, Ireland
- 4Department of Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, Tennessee, USA
- 5Department of Medicine, School of Medicine, Vanderbilt University, Nashville, Tennessee, USA
- 6Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University, Nashville, Tennessee, USA
- 7Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Correspondence to Dr Jimeng Sun, IBM TJ Watson Research Center, PO Box 218, 1101 Kitchawan Road, Yorktown Heights, NY 10598, USA;
- Received 26 May 2013
- Revised 29 July 2013
- Accepted 11 August 2013
- Published Online First 17 September 2013
Objective Common chronic diseases such as hypertension are costly and difficult to manage. Our ultimate goal is to use data from electronic health records to predict the risk and timing of deterioration in hypertension control. Towards this goal, this work predicts the transition points at which hypertension is brought into, as well as pushed out of, control.
Method In a cohort of 1294 patients with hypertension enrolled in a chronic disease management program at the Vanderbilt University Medical Center, patients are modeled as an array of features derived from the clinical domain over time, which are distilled into a core set using an information gain criteria regarding their predictive performance. A model for transition point prediction was then computed using a random forest classifier.
Results The most predictive features for transitions in hypertension control status included hypertension assessment patterns, comorbid diagnoses, procedures and medication history. The final random forest model achieved a c-statistic of 0.836 (95% CI 0.830 to 0.842) and an accuracy of 0.773 (95% CI 0.766 to 0.780).
Conclusions This study achieved accurate prediction of transition points of hypertension control status, an important first step in the long-term goal of developing personalized hypertension management plans.