The paper introduces structured evidence routing for incident risk prediction using multimodal longitudinal electronic health records (EHRs). It proposes a router‑predictor‑reviewer workflow that condenses full patient records into compact summaries and targeted evidence slices, enabling a predictor to generate evidence‑linked risk assessments that a reviewer can critique. Experiments on five one‑year incident diagnosis tasks show that the method matches the AUROC of established supervised EHRSHOT baselines and remains competitive on AUPRC, while providing a patient‑specific evidence trail.
arXiv:2506. 04831v3 Announce Type: replace Abstract: Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data.
arXiv:2602. 00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages.
TRACER is a training‑free multimodal harness that builds minute‑level action records from continuous video and merges them with clinical data into hourly reports, enabling an LLM agent to query and inspect both modalities. In evaluation, TRACER achieved up to 84.8% accuracy on action detection and 61.0% accuracy on clinician‑informed QA, outperforming the strongest baseline. Across 280 patients and ~60,000 camera‑hours, TRACER detected significantly less purposeful activity in patients with a positive delirium assessment compared to those without (p < 0.0001).
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:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
arXiv:2605.20292v2 Announce Type: replace Abstract: Numerical time-series models effectively process irregular electronic health record (EHR) trajectories, but do not expose which temporal patterns s...
arXiv:2609.18852v1 Announce Type: new Abstract: Longitudinal electronic health records (EHRs) capture years of patient history across notes, codes, labs, and procedures, and contain evidence needed t...
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).
RASPER is a reward‑aligned summarizer that tailors the extraction of information from unstructured discharge notes to improve downstream clinical predictions. It uses a tunable LLM summarizer trained with reinforcement learning, where the reward comes from the loss of a downstream predictor, and incorporates patient‑specific context via a longitudinal encoder that soft‑prompts the summarizer with structured codes. The approach consistently outperforms strong baselines on readmission prediction and medication recommendation tasks in the MIMIC‑III and MIMIC‑IV datasets.
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.
CliniCIRCA is a modular large‑language‑model framework that reconstructs longitudinal mental‑health patient journeys from raw electronic health record narratives. It temporally classifies clinical events in unstructured discharge summaries without explicit timestamps, producing 15,891 tagged events from 52 summaries and correcting 629 errors to create verified gold‑standard timelines. The framework then generates temporally grounded summaries, compressing each source by 1.52×, and scales to produce 1,000 silver‑standard timelines for training, showing that instruction tuning improves event extraction, temporal tagging, and summarization across models.
arXiv:2607. 15447v1 Announce Type: new Abstract: Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilising modern representation learning methods.