论文标题
RMEXPLORER：一种视觉分析方法，探索疾病风险模型在人群亚组上的公平性和公平性
RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups
论文作者
论文摘要
疾病风险模型可以识别高危患者，并帮助临床医生提供更多个性化的护理。但是，在一个数据集上开发的风险模型可能不会在不同数据集中患者的不同亚群中推广，并且可能具有意外的性能。对于临床研究人员来说，在没有任何工具的情况下检查跨不同子组的风险模型是一个挑战。因此，我们开发了一个称为Rmexplorer（风险模型探索器）的交互式可视化系统，以实现交互式风险模型评估。具体而言，该系统允许用户通过选择临床，人口统计或其他特征来定义患者的子组，以探索子组风险模型的性能和公平性，并了解对风险评分的特征贡献。为了证明该工具的实用性，我们进行了一个案例研究，在该案例研究中，我们使用Rmexplorer通过将它们应用于445,329个人的英国生物库数据集，以探索三个房颤风险模型。 RMEXPLORER可以帮助研究人员评估其数据中感兴趣的亚群的风险模型的绩效和偏见。
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.
