arXiv:2609.24086v1 Announce Type: cross
Abstract: Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses. We study target-population infere...
By Huali Zhao (School of Mathematics and Statistics, Huazhong University of Science and Technology), Ke Deng (Department of Statistics and Data Science, Tsinghua University)
arXiv:2607. 26752v1 Announce Type: new Abstract: Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under interventions, supporting downstream tasks from imaging-based diagnosis to digital-twin treatment planning.
By Behraj Khan, Shabir Ahmad, Syed Ahmad Chan Bukhari, Tahir Qasim Syed
arXiv:2605.04469v2 Announce Type: replace-cross
Abstract: Large-scale population-level datasets, such as the UK Biobank and the All of Us Research Program, often lack covariates needed for a specific...
By Huali Zhao, Tianying Wang
The paper introduces a missingness‑aware conformal calibration method for mortality prediction that accounts for cross‑hospital distribution shifts. By selecting a measurement on an independent sample, grouping patients by whether that measurement is recorded, and applying Mondrian calibration within each group, the method avoids reusing calibration outcomes. Experiments on eICU and MIMIC‑IV data show that, compared to pooled calibration, it reduces the worst‑group coverage gap by a median of 1.9 percentage points across six settings, though the benefit varies with predictor and hospital.
By Liang You, Dongwen Ou, Hengyu Shi, Siyuan Dai
arXiv:2608. 15783v1 Announce Type: cross Abstract: In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are unobserved.
By Longtian Shi, Molei Liu, Doudou Zhou
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.
By Kara Liu, Maggie Wang, Russ B. Altman