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.
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.
arXiv:2606. 02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model structured longitudinal electronic health records (EHRs).
arXiv:2604. 25605v2 Announce Type: replace-cross Abstract: Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keywords, offers advantages for retrieval of clinical information.
The paper introduces CAST, a concept-guided artifact suppression tuning framework that uses sparse autoencoders to identify and suppress note-specific artifacts in clinical language models. CAST labels latent features with an LLM-assisted pipeline and ICD‑10 constraints, then fine‑tunes the model while providing post‑hoc per‑concept attributions for auditability. In experiments on MIMIC‑IV discharge‑note mortality prediction, CAST outperforms standard fine‑tuned encoders and competes with strong LLM baselines while offering a feature‑level audit trail of clinical concepts and suppressed artifacts.
arXiv:2608. 00935v1 Announce Type: new Abstract: Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning.
arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.
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.
The paper introduces the General Demographic Pre-trained (GDP) model, a lightweight foundation model that learns representations from the two most common clinical attributes—age and sex. By optimizing encoding and visit‑reordering strategies, GDP embeddings are shown to improve predictive performance when concatenated with raw features across various disease and geographic cohorts. The model outperforms several state‑of‑the‑art tabular foundation models and tree‑based algorithms, demonstrating that enriched demographic embeddings can enhance classification tasks while remaining fully compatible with standard classifiers.
The paper introduces UdonCare, a hierarchy‑pruning method that iteratively partitions patients into latent domains using medical ontologies, aiming to improve domain generalization in clinical prediction tasks. It addresses challenges of missing domain labels and lack of clinical insight by discovering hierarchy‑grounded patient domains. Experiments on MIMIC‑III, MIMIC‑IV, and eICU datasets show UdonCare outperforms eight baseline methods across four prediction tasks with significant domain gaps.