title: Predicting changes in systolic blood pressure using longitudinal patient records.
abstract: OBJECTIVE
  This paper introduces a model that predicts future changes in systolic blood pressure (SBP) based on structured and unstructured (text-based) information from longitudinal clinical records.
  
  
  METHODS
  For each patient, the clinical records are sorted in chronological order and SBP measurements are extracted from them. The model predicts future changes in SBP based on the preceding clinical notes. This is accomplished using least median squares regression on salient features found using a feature selection algorithm.
  
  
  RESULTS
  Using the prediction model, a correlation coefficient of 0.47 is achieved on unseen test data (p<.0001). This is in contrast to a baseline correlation coefficient of 0.39.
authors.affiliation: University of North Texas, Denton, TX, United States. Electronic address: John.Solomon@my.UNT.edu.
authors.forename: John Wes
authors.initials: JW
authors.lastname: Solomon
authors.affiliation: University of North Texas, Denton, TX, United States. Electronic address: Rodney.Nielsen@UNT.edu.
authors.forename: Rodney D
authors.initials: RD
authors.lastname: Nielsen
mesh_terms: D001794:Blood Pressure; D001795:Blood Pressure Determination / Q000379:methods* / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D055991:Health Records, Personal; D006801:Humans; D008137:Longitudinal Studies; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Blood pressure; Clinical; Informatics; NLP; Prediction; SBP