This work was supported in part by US Department of Energy Los Alamos National Laboratory contract 47145 and UT-Battelle LLC contract 4000159447 program manager Laura Biven.

There is a growing trend of applying machine learning methods to medical datasets in order to predict patients’future status.Although some of these methods achieve high performance,challenges still exist in comparing and evaluating different models through their interpretable information.Such analytics can help clinicians improve evidence-based medical decision making.In this work,we develop a visual analytics system that compares multiple models’prediction criteria and evaluates their consistency.With our system,users can generate knowledge on different models’inner criteria and how confidently we can rely on each model’s prediction for a certain patient.Through a case study of a publicly available clinical dataset,we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.