Skip to content
Review

Uncoded Clinical Features from Multilingual Electronic Health Records in Catalonia: Development and Validation Study

Sep 2026 · medRxiv · 0 citations
Medicine

TL;DR

A privacy-preserving local hybrid framework combining a compact open-weight SLM with regular expression rules achieves high specificity and precision for extracting six uncoded clinical features from multilingual primary care narratives.

View source

Similar papers

Review Open access Apr 2026

Development of a Framework for Deidentified Japanese Electronic Health Record Narratives Using BERT: Balancing Privacy Protection and Reproducible Entity Extraction in Real-World Data.

This framework enables the generation of deidentified Japanese EHR narratives that preserve contextual structure for auditing while supporting structured entity extraction, thereby addressing the trade-off between privacy protection and reproducibility in real-world data research.

Nobuo Mochizuki, Atsushi Ikeda, Hideyuki Ohshima et al. · 0 citations
Review Open access Sep 2026

From electronic health records to real-world evidence: Savana Next-Generation Registry digital solution

SNGR demonstrates how unstructured EHR data can be transformed into analysis-ready datasets for RWE generation, and provides a scalable and methodologically robust infrastructure for next-generation registries, particularly suited for oncology and other data-rich clinical domains.

J. Marin-Corral, N. Iglesias, S. Menke et al. · 0 citations
#small language model Open access Aug 2026

The Performance of Large Language Models in Extracting Intestinal Symptoms From Electronic Health Records: Retrospective Observational Study

This study provides a systematic comparison of several open-source LLMs on a structured intestinal symptom extraction task and concludes that Qwen3 models offer a favorable balance between accuracy and efficiency, making them suitable for resource-constrained scenarios.

Xin-Yue Zhang, Quan-Yu Wang, Beibei Liu et al. · 0 citations
Open access Sep 2026

A Scalable Method for Validated Data Extraction from Electronic Health Records with Large Language Models

Health care organizations increasingly require structured, patient-level clinical variables for treatment decisions, operational workflows, quality measurement, and clinical trial screening. Relevant information is often fragmented across heterogeneous electronic health record (EHR) systems, unstructured formats, a...

Timothy J. Stuhlmiller, AJ Rabe, Jeff Rapp et al. · 0 citations
Open access Oct 2026

Structured Prompt-Based NLP for Infection-Related Classification in Clinical Documentation

Infectious diseases remain a major contributor to morbidity in hospital settings, and clinically relevant information is often embedded within unstructured Electronic Health Record (EHR) narratives. This study presents a structured prompt-based Large Language Model (LLM) Natural Language Processing (NLP) pipeline for p...

G. Alfattni · 0 citations
Review Open access Oct 2026

Guideline-grounded large language models for extracting genome-informed clinical recommendations from electronic health records.

BACKGROUND Precision medicine requires delivery and tracking of genome-informed risk assessments (GIRAs), often documented in unstructured electronic health record (EHR) notes, making large-scale evaluation reliant on labor-intensive manual chart review. Large language models (LLMs) offer a promising approach to automa...

Yi Xin, K. W. Davis, Wu-Chen Su et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.