arXiv Machine Learning
Diagnosing Low-Confidence Patient Profiles via Localized Harmful Covariate Shift Detection
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Metamorphic Testing for Clinical ML Models: A Framework Proposal and Pilot Study
arXiv:2607. 22984v1 Announce Type: cross Abstract: Machine learning models for clinical prediction tasks, such as in-hospital mortality and sepsis onset, routinely achieve high AUROC scores.
arXiv AI
Jul 10
SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data
arXiv:2607. 07725v1 Announce Type: cross Abstract: Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment.
arXiv Machine Learning
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Local Discrimination of Binary Classifiers via Localized AUC Functional
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arXiv Machine Learning
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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 AI
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RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcare
arXiv:2605. 12895v2 Announce Type: replace-cross Abstract: Clinical decision-support systems are expert systems whose recommendations clinicians act on directly, yet they are usually cleared on one aggregate accuracy number from a held-out test set.
arXiv AI
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FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records
arXiv:2505. 16941v4 Announce Type: replace-cross Abstract: Foundation models (FMs) promise to address core limitations of traditional supervised machine learning: (i) reliance on large amounts of labeled data, (ii) task specificity, and (iii) poor transportability.