The paper introduces a new metric called Localized AUC (LAUC), a weighted version of the traditional AUC, to detect performance heterogeneity in risk prediction models. Using LAUC, the authors develop a method named LOCATOR that identifies patient cohorts where the model’s predictions are inaccurate. They validate LOCATOR on both real-world data and simulations and provide a statistical foundation for applying LAUC in this context.
arXiv:2609.08379v1 Announce Type: new Abstract: Accurate diagnostic and risk-prediction models are important for supporting clinical decision-making during infectious disease outbreaks. However, priv...
The paper introduces Geographically Regularized AUC-Maximizing Personalized Federated Learning (GrAUC-PFL), a method that directly optimizes a smooth pairwise AUC surrogate to train personalized models while keeping patient data local. It incorporates graph-based regularization so that geographically neighboring institutions share similar coefficient vectors, thereby addressing institutional heterogeneity. Experiments on simulations and real data demonstrate improved discriminative performance, especially when neighboring institutions have similar data-generating characteristics.
arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.
arXiv:2610.19178v1 Announce Type: new Abstract: Before deploying clinical prediction models, their reliability must be assessed to ensure safe use. Errors may arise from distribution shifts between t...
arXiv:2607. 28655v1 Announce Type: cross Abstract: Small-area estimation (SAE) enables researchers and policymakers to identify spatial disparities in health outcomes, but survey-based SAE products carry an inherent lag.
arXiv:2602.04775v2 Announce Type: replace Abstract: In high-stakes risk prediction, interval-valued predictions provide a natural way to represent predictive uncertainty. However, standard evaluation...
arXiv:2606. 28623v1 Announce Type: new Abstract: Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts.
arXiv:2509. 10517v3 Announce Type: replace Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use.
arXiv:2512. 13003v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is essential for determining when a supervised model encounters inputs that differ meaningfully from its training distribution.
arXiv:2608. 14683v1 Announce Type: new Abstract: Given a patient's clinical findings, a diagnostic system ranks possible diseases and must decide when to endorse its first prediction or defer it for review.
arXiv:2606. 00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models.
The paper demonstrates that publicly available clinical machine learning models, such as logistic regression (LR), pose significant privacy risks because attackers can recover model parameters and identify training cohorts through various membership inference attacks. The authors show that even small cohorts can be reliably identified and that common practices like cross-validation can worsen the risk. To mitigate this, they propose a quantum-inspired defense that tensorizes discretized models into tensor trains (TTs), which obfuscates parameters, preserves accuracy, and maintains interpretability while providing black‑box protection comparable to Differential Privacy.