Article

Comparison of local large language models for extraction of signs and symptoms data from electronic health records

2025-12-16

Abstract excerpt

Electronic health records (EHRs) provide a large source of data that can be used for research purposes. Extraction of information from unstructured clinical notes in EHRs can be automated by large language models (LLMs). Although LLMs are promising for this task, challenges remain in reliable application of LLMs to EHR, including the lack of development and validation for languages other than English. Here, we ide...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 43eed226-4413-5487-8ede-8c65f059dc57
DOI: 10.64898/2025.12.11.25341954

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Comparison of local large language models for extraction of signs and symptoms data from electronic health recordsDOI 10.64898/2025.12.11.25341954Read

Comparison of Large Language Models Versus Traditional Information Extraction Methods for Real World Evidence of Patient Symptomatology in Acute and Post-Acute Sequelae of SARS-CoV-2Semantic proximity Read

Longitudinal information extraction from clinical notes in rare diseases: an efficient approach with small language modelsSemantic proximity Read

Large language models for accurate disease detection in electronic health recordsSemantic proximity Read

Natural Language Processing for Clinical Laboratory Data Repository Systems: Implementation and Evaluation for Respiratory VirusesSemantic proximity Read

Scalable information extraction from free text electronic health records using large language modelsSemantic proximity Read

Developing an open-source framework for LLM evaluation of patients using EHR clinical documentation; performance of LLMs relative to medical professionalsSemantic proximity Read

A comparison of large language model versus manual chart review for extraction of data elements from the electronic health recordSemantic proximity Read

On the limitations of large language models in clinical diagnosisSemantic proximity Read

Combining Clinician Expertise with Prompt Engineering enhances Small Language Models Reliability for Cancer Entity Recognition in Electronic Health RecordsSemantic proximity Read

Evaluation of Large Language Model Performance on the Biomedical Language Understanding and Reasoning Benchmark: Comparative StudySemantic proximity Read

Grounded large language models for diagnostic prediction in real-world emergency department settingsSemantic proximity Read