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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., Comment: IEEE VIS 2022 Short

Coronary artery disease (CAD) surveillance has led to the development of risk stratification tools that drive clinical mitigation efforts for CAD risk. As common risk assessment tools, including the Framingham Risk Score (FRS) or Pooled Cohort Risk Equations, have insufficient accuracy on an individual level to meet NASA’s stringent screening guidelines, NASA has required new approaches to address the risk of catastrophic cardiovascular (CV) medical events among astronauts. Coronary artery calcium (CAC) scanning has emerged as the current best tool to enhance CV risk assessment in asymptomatic individuals, with risk information incremental to FRS data.