arXiv:2505. 00571v3 Announce Type: replace-cross Abstract: Machine Learning (ML) is gaining popularity in epidemiology and healthcare studies for hypothesis-free discovery of risk and protective factors.
arXiv:2609.14902v1 Announce Type: cross Abstract: Shapley value (SV)-based methods are the prevailing framework for feature attribution in machine learning, yet existing population-level Shapley esti...
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: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 introduces Bayesian Propensity Score Predictive Inference (PSPI), an interaction‑based outcome modeling framework that uses Bayesian additive regression trees and natural cubic splines of the sampling propensity score to extend randomized trial results to broader target populations. PSPI includes variants that apply splines to the treatment effect component alone or to both prognostic and treatment effect components, enabling separate regularization of selection‑related variation and explicit extrapolation beyond boundary knots. Simulation studies demonstrate that spline‑based PSPI models outperform competing methods, achieving lower root mean squared errors and near‑nominal coverage, especially under substantial covariate shift, and in the CODIACS Vanguard trial application PSPI estimates larger intervention benefits than the unadjusted trial estimate.
arXiv:2606. 07677v1 Announce Type: cross Abstract: Electronic health records (EHR) pose large-scale multi-disease modeling problems in which many outcomes are rare and strongly influenced by shared risk factors.
arXiv:2601. 20819v2 Announce Type: replace-cross Abstract: Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science.
arXiv:2606. 09860v1 Announce Type: cross Abstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks.
arXiv:2606. 27269v1 Announce Type: cross Abstract: Reliably quantifying predictive uncertainty is difficult for complex, high-dimensional, or misspecified models.
arXiv:2607. 00733v1 Announce Type: cross Abstract: Mechanistic modeling via ordinary differential equations (ODEs) provides interpretable descriptions of complex dynamics and enables inference of underlying mechanisms, which is particularly valuable in clinical settings.
The paper introduces Sources of Uncertainty (SoU) inference, a framework that extends Bayesian inference by allowing heterogeneous confidence to be encoded across model components. This selective treatment controls the uncertainty retained in each component and its propagation, yielding Empirical Bayes and profiling-type inference as special cases. The authors also present a general method for inducing sparsity, demonstrating its application in linear and logistic regression and in Bayesian neural networks for self‑pruning.
The study presents a clinically interpretable machine‑learning framework that estimates biological age (BA) using a CatBoost regression model trained on 44 diverse predictors from NHANES data. SHAP values are leveraged not only for visual interpretation but also through an automated quantitative procedure that identifies feature‑value ranges linked to the lowest and highest BA contributions, including zero‑crossing thresholds. The resulting BA estimates correlate strongly with chronological age and highlight key predictors such as blood urea nitrogen, serum glucose, and vigorous activity, offering transparent, reproducible insights for precision health research.
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.