JMIR AI · 2026
A Simplified Metric to Streamline Between-Group Fairness Assessment for Predictive Models: Algorithm Development and Evaluation Study.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Haoyuan, Hong Chuan, Pencina Michael, Engelhard Matthew
Аннотация
BACKGROUND: Fairness evaluation is essential for trustworthy clinical risk prediction. However, existing fairness-oriented discrimination metrics either ignore cross-group comparisons or rely on exhaustive pairwise evaluations, making them difficult to interpret and impractical for model selection.
OBJECTIVE: This study aimed to develop and evaluate novel fairness-oriented discrimination metrics for clinical risk prediction that address limitations of within-group and pairwise cross-group approaches.
METHODS: We examined theoretical properties of existing U-statistic-based metrics, including concordance index (CI) and area under the receiver operating characteristic curve (AUC), when applied to subgroups. We highlighted the distinction between within-group discrimination (ranking within a subgroup) and group-level discrimination (ranking relative to the broader population). Building on this framework, we proposed group-level extensions of the CI and AUC that summarize subgroup-specific performance in a single interpretable measure. We then applied these metrics to the PREVENT (Predicting Risk of Cardiovascular Disease Events) equation, a recently developed model for atherosclerotic cardiovascular disease.
RESULTS: The traditional subgroup-specific CI and AUC captured within-group but not group-level discrimination, obscuring inequities in clinical decision-making. Existing cross-group approaches (eg, the xCI and xAUC metrics) addressed this limitation but became computationally and interpretively burdensome with multiple subgroups due to pairwise comparisons. Our proposed metrics provided a streamlined alternative, yielding 1 summary statistic per subgroup while retaining sensitivity to cross-group ranking disparities. Applied to PREVENT, these metrics revealed differences in subgroup performance not apparent from within-group evaluations.
CONCLUSIONS: By distinguishing between within-group and group-level discrimination, our framework clarifies a common source of misinterpretation in fairness evaluation. The proposed group-level extensions of the CI and AUC provide practical, interpretable tools for evaluating fairness in clinical prediction models, enabling more transparent and equitable risk assessment.
OBJECTIVE: This study aimed to develop and evaluate novel fairness-oriented discrimination metrics for clinical risk prediction that address limitations of within-group and pairwise cross-group approaches.
METHODS: We examined theoretical properties of existing U-statistic-based metrics, including concordance index (CI) and area under the receiver operating characteristic curve (AUC), when applied to subgroups. We highlighted the distinction between within-group discrimination (ranking within a subgroup) and group-level discrimination (ranking relative to the broader population). Building on this framework, we proposed group-level extensions of the CI and AUC that summarize subgroup-specific performance in a single interpretable measure. We then applied these metrics to the PREVENT (Predicting Risk of Cardiovascular Disease Events) equation, a recently developed model for atherosclerotic cardiovascular disease.
RESULTS: The traditional subgroup-specific CI and AUC captured within-group but not group-level discrimination, obscuring inequities in clinical decision-making. Existing cross-group approaches (eg, the xCI and xAUC metrics) addressed this limitation but became computationally and interpretively burdensome with multiple subgroups due to pairwise comparisons. Our proposed metrics provided a streamlined alternative, yielding 1 summary statistic per subgroup while retaining sensitivity to cross-group ranking disparities. Applied to PREVENT, these metrics revealed differences in subgroup performance not apparent from within-group evaluations.
CONCLUSIONS: By distinguishing between within-group and group-level discrimination, our framework clarifies a common source of misinterpretation in fairness evaluation. The proposed group-level extensions of the CI and AUC provide practical, interpretable tools for evaluating fairness in clinical prediction models, enabling more transparent and equitable risk assessment.