The study examined whether adding patient‑reported survey data to electronic health records (EHRs) improves the prediction of a first opioid use disorder (OUD) diagnosis. Using 267,747 All of Us participants, the authors compared EHR‑only models to EHR+survey models across multiple machine‑learning algorithms and look‑back windows. Survey augmentation consistently increased predictive performance, with the best 24‑month LightGBM model’s PR‑AUC rising from 0.6219 to 0.6603, and survey features ranked as the second most important information domain.
arXiv:2608. 04180v1 Announce Type: new Abstract: Feature selection is a critical step in electronic health record (EHR)-based predictive modeling, where input variables are often high-dimensional, sparse, noisy, and redundant.
arXiv:2609.40108v1 Announce Type: new Abstract: Opioid overdose remains a major clinical and public health burden, highlighting the need for scalable approaches to identify patients at high risk. Her...
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.
EHR2Trace is a system that transforms electronic health records from multiple sources into a standardized, traceable event format suitable for training and evaluating patient world models and clinical agents. It links each event to its original record, separates the event time from the time the information became available, and distinguishes between medication orders, dispensing, and administration. The tool supports both OMOP and MEDS data models, includes automated validation, and was tested on three clinical datasets, converting 846.4 million events and detecting all injected faults.
arXiv:2411. 15240v5 Announce Type: replace-cross Abstract: Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends.
arXiv:2608. 16273v1 Announce Type: cross Abstract: Foresight-England (Foresight-E) is the first national-scale generative foundation model of electronic health records (EHRs), developed as a research pilot strictly for COVID-19 research.
arXiv:2509. 24118v2 Announce Type: replace Abstract: Electronic health Records (EHRs) have become a cornerstone in modern-day healthcare.
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:2608. 06430v1 Announce Type: new Abstract: Learning from Electronic Health Records (EHRs) has gained significant attention due to its potential to improve clinical prediction.
arXiv:2608. 16087v1 Announce Type: cross Abstract: Removing wearables from physiological monitoring also removes their supervision: the signal indicating where and when a stress response occurred.
arXiv:2609.39371v1 Announce Type: new Abstract: In hospital workflows, electronic health records (EHRs) are often noisy, and may not contain the evidence needed to confirm events or measurements refe...
arXiv:2608. 08920v1 Announce Type: new Abstract: Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited interpretability.