A Visual Analytics System for Multi-model Comparison on Clinical Data Predictions
Authors:
Preprint
Abstract
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.Keywords
All Related Versions
This publication has 27 references indexed in Scilit:
- Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantationBiomedical Signal Processing and Control, 2019
- The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model*Critical Care Medicine, 2018
- Recurrent Neural Networks for Multivariate Time Series with Missing ValuesScientific Reports, 2018
- The randomized information coefficient: assessing dependencies in noisy dataMachine Learning, 2017
- Towards better analysis of machine learning models: A visual analytics perspectiveVisual Informatics, 2017
- A machine learning-based framework to identify type 2 diabetes through electronic health recordsInternational Journal of Medical Informatics, 2017
- MIMIC-III, a freely accessible critical care databaseScientific Data, 2016
- Interpreting Random Forest Classification Models Using a Feature Contribution Method2014
- Prediction of 3‐yr cadaveric graft survival based on pre‐transplant variables in a large national datasetClinical Transplantation, 2003
- On the posterior-probability estimate of the error rate of nonparametric classification rulesIEEE Transactions on Information Theory, 1994