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arXiv:2610.18672v1 Announce Type: cross Abstract: Biomedical language models (LMs) provide strong representations for clinical tasks, but they often fail to capture the hierarchical structure among m...
arXiv:2606. 24102v1 Announce Type: cross Abstract: Most electronic health record (EHR) foundation models encode clinical events as discrete event tokens from a fixed vocabulary and therefore cannot directly represent events containing unseen concepts or new combinations of concepts and attributes such as numeric values.
arXiv:2608. 20315v1 Announce Type: new Abstract: Predictive models over structured electronic health records (EHRs) remain central to machine learning for healthcare, but few have jointly emphasized quantitative laboratory information and interpretability with respect to input medical events.
The paper introduces MEDIATOR, a discrete, ontology‑agnostic tokenizer that maps any clinical concept—whether encoded, synonymous, or free‑text—into a shared vector‑quantized codebook. By collapsing equivalent expressions and organizing distinct concepts in a medically meaningful space, MEDIATOR serves as the token interface for an EHR Transformer, boosting mean AUPRC by 4.0 points on 20 MIMIC‑III tasks and 8.6 points in zero‑shot transfer to 14 eICU tasks, while enabling full event coverage and transferable attribution across hospitals.
arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.
arXiv:2606. 28798v1 Announce Type: new Abstract: Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrate diagnostic signals from both clinical narratives and structured electronic health record (EHR) variables.