arXiv Machine Learning
R-GEAN: Regimen-Guided Edit Action Network for Within-Admission Medication Change Prediction
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arXiv AI
Sep 3
The Ceiling Is in the Channel: Auditing Learner Gaps and Measurement Frontiers in Clinical Prediction
The paper introduces a framework that distinguishes two causes of saturation in clinical prediction: a learner gap, where the model fails to use available information, and a measurement‑channel ceiling, where the recorded variables limit performance. It provides theoretical characterizations, finite‑sample diagnostics, and empirical audits across three large cohorts, showing that well‑tuned models approach the frontier while deficient learners leave large gaps. A PRISMA‑guided synthesis across 104 tasks reveals consistent channel‑level patterns, suggesting that improving the learner or the measurement channel can audit and potentially lift performance.
arXiv Machine Learning
Jul 30
CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation
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.
arXiv Machine Learning
Sep 22
Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder
The paper evaluates machine learning models that predict retention and premature discontinuation in medication for opioid use disorder (MOUD). Using the Treatment Episode Data Set-Discharges (TEDS‑D) from 2015‑2019, the authors trained four models and examined overall performance as well as subgroup error rates by race, ethnicity, age, and sex. They also tested bias‑mitigation techniques, finding that these can reduce but not eliminate performance gaps without harming predictive accuracy.
arXiv AI
Jul 24
A Knowledge-Injection Framework for Zero-Shot Adaptation of LLMs to Delirium Prediction
arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.
arXiv Machine Learning
Jun 17
The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance
arXiv:2606. 17113v1 Announce Type: new Abstract: Distinguishing causal adverse drug events (ADEs) from spurious correlations remains a central challenge in pharmacovigilance.
arXiv AI
Aug 3
EarlyDx: An Admission-Anchored Benchmark for Open-Ended Generation of Evidence-Supported ED-Encounter Diagnoses
arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.