title: Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes: An Application of Machine Learning Using Electronic Health Records.
abstract: BACKGROUND
  Application of novel machine learning approaches to electronic health record (EHR) data could provide valuable insights into disease processes. We utilized this approach to build predictive models for progression to prediabetes and type 2 diabetes (T2D).
  
  
  METHODS
  Using a novel analytical platform (Reverse Engineering and Forward Simulation [REFS]), we built prediction model ensembles for progression to prediabetes or T2D from an aggregated EHR data sample. REFS relies on a Bayesian scoring algorithm to explore a wide model space, and outputs a distribution of risk estimates from an ensemble of prediction models. We retrospectively followed 24 331 adults for transitions to prediabetes or T2D, 2007-2012. Accuracy of prediction models was assessed using an area under the curve (AUC) statistic, and validated in an independent data set.
  
  
  RESULTS
  Our primary ensemble of models accurately predicted progression to T2D (AUC = 0.76), and was validated out of sample (AUC = 0.78). Models of progression to T2D consisted primarily of established risk factors (blood glucose, blood pressure, triglycerides, hypertension, lipid disorders, socioeconomic factors), whereas models of progression to prediabetes included novel factors (high-density lipoprotein, alanine aminotransferase, C-reactive protein, body temperature; AUC = 0.70).
  
  
  CONCLUSIONS
  We constructed accurate prediction models from EHR data using a hypothesis-free machine learning approach. Identification of established risk factors for T2D serves as proof of concept for this analytical approach, while novel factors selected by REFS represent emerging areas of T2D research. This methodology has potentially valuable downstream applications to personalized medicine and clinical research.
authors.affiliation: GNS Healthcare, Cambridge, MA, USA janderson@gnshealthcare.com jpa696@mail.harvard.edu.
authors.forename: Jeffrey P
authors.initials: JP
authors.lastname: Anderson
authors.affiliation: GNS Healthcare, Cambridge, MA, USA.
authors.forename: Jignesh R
authors.initials: JR
authors.lastname: Parikh
authors.affiliation: GNS Healthcare, Cambridge, MA, USA.
authors.forename: Daniel K
authors.initials: DK
authors.lastname: Shenfeld
authors.affiliation: GNS Healthcare, Cambridge, MA, USA.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Ivanov
authors.affiliation: GNS Healthcare, Cambridge, MA, USA.
authors.forename: Casey
authors.initials: C
authors.lastname: Marks
authors.affiliation: GNS Healthcare, Cambridge, MA, USA.
authors.forename: Bruce W
authors.initials: BW
authors.lastname: Church
authors.affiliation: GNS Healthcare, Cambridge, MA, USA.
authors.forename: Jason M
authors.initials: JM
authors.lastname: Laramie
authors.affiliation: Pfizer Inc, New York, NY, USA.
authors.forename: Jack
authors.initials: J
authors.lastname: Mardekian
authors.affiliation: Pfizer Inc, New York, NY, USA.
authors.forename: Beth Anne
authors.initials: BA
authors.lastname: Piper
authors.affiliation: Pfizer Inc, New York, NY, USA.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Willke
authors.affiliation: Pfizer Inc, New York, NY, USA.
authors.forename: Dale A
authors.initials: DA
authors.lastname: Rublee
mesh_terms: D000328:Adult; D019540:Area Under Curve; D003924:Diabetes Mellitus, Type 2; D018450:Disease Progression; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008490:Medical Informatics / Q000379:methods; D011236:Prediabetic State; D012372:ROC Curve; D012189:Retrospective Studies; D012307:Risk Factors
keywords: diabetes mellitus; disease progression; electronic health records; medical informatics; prediabetic state; type 2