# RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups

arXiv: https://arxiv.org/abs/2209.06378 · HTML: https://mlsanity.com/paper/2209.06378/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2022.

## Built on by (most cited first, 6 of 6)

- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Models](https://mlsanity.com/paper/2302.07371/) (2023)
- [Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models](https://mlsanity.com/paper/2305.16937/) (2023)
- [ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinement](https://mlsanity.com/paper/2309.11155/) (2023)
- [Latent Space Explorer: Visual Analytics for Multimodal Latent Space Exploration](https://mlsanity.com/paper/2312.00857/) (2023)
- [VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions](https://mlsanity.com/paper/2307.14448/) (2023)

## Abstract (arXiv)

Disease risk models can identify high-risk patients and help clinicians provide more personalized care. However, risk models developed on one dataset may not generalize across diverse subpopulations of patients in different datasets and may have unexpected performance. It is challenging for clinical researchers to inspect risk models across different subgroups without any tools. Therefore, we developed an interactive visualization system called RMExplorer (Risk Model Explorer) to enable interactive risk model assessment. Specifically, the system allows users to define subgroups of patients by selecting clinical, demographic, or other characteristics, to explore the performance and fairness of risk models on the subgroups, and to understand the feature contributions to risk scores. To demonstrate the usefulness of the tool, we conduct a case study, where we use RMExplorer to explore three atrial fibrillation risk models by applying them to the UK Biobank dataset of 445,329 individuals. RMExplorer can help researchers to evaluate the performance and biases of risk models on subpopulations of interest in their data.