Article

Optimal strategies for adapting open-source large language models for clinical information extraction: a benchmarking study in the context of ulcerative colitis research

2024-11-07

Abstract excerpt

<h4>Background</h4> Closed-source large language models (LLMs) like GPT-4o have shown promise for clinical information extraction but are potentially limited by cost, data security concerns, and inflexibility. Open-source models have emerged as an attractive alternative, with many LLM adaptation strategies developed in the literature. However, it is currently unclear what adaptation strategies are optimal, and how...

Topics

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

Identifiers and source

Literature Corpus work: 2f32e74f-a5bf-5ba9-bf06-f10868a547e6
DOI: 10.1101/2024.11.06.24316817

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Optimal strategies for adapting open-source large language models for clinical information extraction: a benchmarking study in the context of ulcerative colitis researchDOI 10.1101/2024.11.06.24316817Read

Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in IBDSemantic proximity Read

Automatic Processing of Gastrointestinal Endoscopy Referrals and Patient Communication Using Large Language ModelsSemantic proximity Read

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

Evaluation of Large Language Models in the Clinical Management of Patients With Upper Gastrointestinal Bleeding : Insights From Real-World Patient DataSemantic proximity Read

Implementing a Resource-Light and Low-Code Large Language Model System for Information Extraction from Mammography Reports: A Case StudySemantic proximity Read

Systematic Review of Natural Language Processing Applied to Gastroenterology & Hepatology: The Current State of the ArtSemantic proximity Read

Accurate, Robust, and Scalable Machine Abstraction of Mayo Endoscopic Subscores from Colonoscopy ReportsSemantic proximity Read

For clinical data extraction, QLoRA attains accuracy close to LoRA while requiring lower compute resourcesSemantic proximity Read

MRQC-LLM: A Novel Large Language Model Framework for Enhancing Medical Record Quality ControlSemantic proximity Read

The Transformative Potential of Large Language Models in Mining Electronic Health Records DataSemantic proximity Read

Natural Language Processing Can Automate Extraction of Barrett’s Esophagus Endoscopy Quality MetricsSemantic proximity Read

RAPID: Reliable and efficient Automatic generation of submission rePorting checklists with Large language moDelsSemantic proximity Read