#ehr-data News & Analysis

5 articles tagged with #ehr-data. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBearisharXiv – CS AI · Jun 197/10

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Before the Labels: How Dataset Construction Shapes Suicidality Detection in Clinical Text

Researchers demonstrate that clinical NLP datasets for suicidality detection, particularly the ScAN dataset built on MIMIC-III notes, embed specific operational choices that obscure how labels are constructed rather than representing objective ground truth. The study reveals that dataset design decisions—including single annotators, ICD-based cohort selection, and hospital-stay aggregation—shape what suicidality means in algorithmic systems, highlighting critical gaps between documented clinical judgments and actual suicidal intent.

AINeutralarXiv – CS AI · Jun 17/10

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EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs

Researchers introduce EHRBench, an automated benchmark containing nearly 1 million QA items derived from real patient electronic health records to evaluate large language models on clinical decision-making tasks. The framework combines LLM-based template generation with knowledge-base verification to assess model performance on diagnosis, treatment, and prognosis at scale while maintaining reliability.

AIBullisharXiv – CS AI · Apr 157/10

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Schema-Adaptive Tabular Representation Learning with LLMs for Generalizable Multimodal Clinical Reasoning

Researchers propose Schema-Adaptive Tabular Representation Learning, which uses LLMs to convert structured clinical data into semantic embeddings that transfer across different electronic health record schemas without retraining. When combined with imaging data for dementia diagnosis, the method achieves state-of-the-art results and outperforms board-certified neurologists on retrospective diagnostic tasks.

AINeutralarXiv – CS AI · Jun 96/10

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TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs

Researchers introduce TRIAGE, an LLM-based framework that uses dialectical reasoning to improve risk prediction on irregularly sampled medical time series data. The approach generates competing clinical outcome rationales to produce calibrated, continuous risk scores rather than overconfident binary predictions, achieving 3.3% AUPRC improvement and 81% reduction in calibration error versus baseline methods.

AINeutralarXiv – CS AI · May 126/10

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WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records

WISTERIA is a machine learning framework that improves clinical AI by treating noisy medical labels as uncertain observations rather than ground truth. By enforcing consistency across multiple weak supervision sources and incorporating medical ontologies, the method achieves better generalization across healthcare institutions and demonstrates robustness to label noise.