title: Visualizing Risk Prediction Models.
abstract: OBJECTIVE
  Risk prediction models can assist clinicians in making decisions. To boost the uptake of these models in clinical practice, it is important that end-users understand how the model works and can efficiently communicate its results. We introduce novel methods for interpretable model visualization.
  
  
  METHODS
  The proposed visualization techniques are applied to two prediction models from the Framingham Heart Study for the prediction of intermittent claudication and stroke after atrial fibrillation. We represent models using color bars, and visualize the risk estimation process for a specific patient using patient-specific contribution charts.
  
  
  RESULTS
  The color-based model representations provide users with an attractive tool to instantly gauge the relative importance of the predictors. The patient-specific representations allow users to understand the relative contribution of each predictor to the patient's estimated risk, potentially providing insightful information on which to base further patient management. Extensions towards non-linear models and interactions are illustrated on an artificial dataset.
  
  
  CONCLUSIONS
  The proposed methods summarize risk prediction models and risk predictions for specific patients in an alternative way. These representations may facilitate communication between clinicians and patients.
authors.affiliation: KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium; iMinds Medical IT, KU Leuven, Leuven, Belgium.
authors.forename: Vanya
authors.initials: V
authors.lastname: Van Belle
authors.affiliation: KU Leuven, Department of Development and Regeneration, Leuven, Belgium.
authors.forename: Ben
authors.initials: B
authors.lastname: Van Calster
mesh_terms: D000369:Aged, 80 and over; D001281:Atrial Fibrillation / Q000175:diagnosis*; D000066491:Clinical Decision-Making; D003196:Computer Graphics; D020000:Decision Support Systems, Clinical; D006801:Humans; D007383:Intermittent Claudication / Q000175:diagnosis*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D018570:Risk Assessment; D012307:Risk Factors