title: Predicting changes in hypertension control using electronic health records from a chronic disease management program.
abstract: 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.
  
  
  METHODS
  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.
authors.affiliation: Healthcare Analytics, IBM TJ Watson Research Center, Yorktown Heights, New York, USA.
authors.forename: Jimeng
authors.initials: J
authors.lastname: Sun
authors.forename: Candace D
authors.initials: CD
authors.lastname: McNaughton
authors.forename: Ping
authors.initials: P
authors.lastname: Zhang
authors.forename: Adam
authors.initials: A
authors.lastname: Perer
authors.forename: Aris
authors.initials: A
authors.lastname: Gkoulalas-Divanis
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Denny
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kirby
authors.forename: Thomas
authors.initials: T
authors.lastname: Lasko
authors.forename: Alexander
authors.initials: A
authors.lastname: Saip
authors.forename: Bradley A
authors.initials: BA
authors.lastname: Malin
mesh_terms: D000959:Antihypertensive Agents / Q000627:therapeutic use; D002908:Chronic Disease; D019468:Disease Management; D057286:Electronic Health Records; D006801:Humans; D006973:Hypertension / Q000628:therapy*; D008962:Models, Theoretical; D011379:Prognosis
chemical_list: D000959:Antihypertensive Agents
keywords: hypertension control; predictive modeling; visualization
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