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

A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions