FRESH (Fusion of Recent Evidence and Subject Histories) is a method that integrates population‑level summary results—such as published clinical trials, registry summaries, prior natural‑history studies, and peer‑reviewed indirect comparisons—into predictive models trained on patient‑level data. It recalibrates a generative model trained on patient data so that its predictions match specified aggregate statistics for a target population, achieving this with minimal perturbation in an information‑geometric sense. The recalibrated model can be used directly or to update the original generative model, enabling applications like contextualizing single‑arm trials, simulating clinical‑trial designs, and conducting comparative‑effectiveness analyses of on‑market therapies.
arXiv:2604. 23904v3 Announce Type: replace-cross Abstract: Synthetic tabular data are often evaluated by distributional similarity, privacy distance, or train-on-synthetic-test-on-real predictive performance, but these criteria do not ensure validity for causal inference.
ReClaim is a family of generative foundation models trained on 43.8 billion administrative claims from over 200 million enrollees in the MarketScan Research Databases. The models jointly learn diagnoses, procedures, medications, and expenditures, and outperform disease‑specific gradient‑boosted models and transformer baselines in predicting more than 1,000 diseases, forecasting next‑year expenditure, and identifying high‑need, high‑cost enrollees. ReClaim’s representations also improve effect estimates in real‑world evidence studies compared with algorithm‑derived covariates, demonstrating the value of nationwide insurance claims as a scalable substrate for healthcare foundation models.
The paper introduces Patient, Place, Prior (P³), an audit framework that evaluates whether a longitudinal medical imaging forecast benefits from a patient’s own imaging history (Patient), from patient‑matched spatial support (Place), and from predictive value beyond a population‑average model (Prior). It applies this framework to Cancer JEPA, a one‑step model that predicts future breast dynamic contrast‑enhanced MRI scans during neoadjuvant therapy, incorporating a lesion‑constrained neural correction trained with an occlusion‑based latent objective. Validation shows lower forecast error when the neural correction uses the patient’s own history and matched lesion maps, though the 95% interval comparing patient‑specific to population‑average corrections includes zero, illustrating that P³ distinguishes input usage from true patient‑specific predictive advantage.
arXiv:2610.02957v1 Announce Type: new Abstract: World models learn state evolution from trajectories, making access to temporal context a central training requirement. Federated learning can use dist...
arXiv:2608. 19501v1 Announce Type: cross Abstract: Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments.
PRISM (Population‑Retrieved Inference for Structured Multimodal Prognosis) is a multimodal language model that integrates structured measurements, physiological waveforms, longitudinal history, and population context for clinical prognosis. It uses per‑feature numerical tokenization, recency‑weighted aggregation of ECGs, and a dual‑modality population memory bank, retrieving mean‑pooled embeddings of similar encounters without including individual records in the prompt. On the 1,443‑task MDS‑ED benchmark, PRISM achieves high AUROCs across diagnosis, deterioration, ICU admission, and mortality, outperforming baselines and demonstrating the value of structured multimodal integration and supervised external adaptation.
arXiv:2610.41368v1 Announce Type: new Abstract: Overly restrictive eligibility criteria contribute to widespread enrollment failure in clinical trials, yet published recommendations for how specific...
arXiv:2609.06294v1 Announce Type: new Abstract: Estimating conditional average treatment effects (CATE) enables efficient targeting of interventions, but many applications have limited experimental s...
arXiv:2606. 06516v1 Announce Type: cross Abstract: We advance a new probabilistic supervised learning approach that permits reliable, automated, and early individualised prediction of the severity with which a disease will develop in a prospective patient.
arXiv:2609.38902v1 Announce Type: cross Abstract: Augmenting small concurrent studies with external or historical cohorts is attractive in drug development, where enrollment is slow, follow-up is exp...
arXiv:2606. 05797v1 Announce Type: new Abstract: Longitudinal treatment decisions require predicting potential outcomes under future treatment sequences in the presence of time-varying confounding, heterogeneous patient dynamics, and limited domain-specific data.
arXiv:2603. 10494v2 Announce Type: replace-cross Abstract: Evidence-grounded generation produces summaries whose claims should be supported by supplied evidence, but claim-level verifiers provide noisy feedback and can reward models that simply say less.