Harnessing Large Language Models for Precision Querying and Retrieval-Augmented Knowledge Extraction in Clinical Data Science
On the map: Retrieval-augmented generation → Medical and biomedical RAG
Abstract (arXiv)
This study applies Large Language Models (LLMs) to two foundational Electronic Health Record (EHR) data science tasks: structured data querying (using programmatic languages, Python/Pandas) and information extraction from unstructured clinical text via a Retrieval Augmented Generation (RAG) pipeline. We test the ability of LLMs to interact accurately with large structured datasets for analytics and the reliability of LLMs in extracting semantically correct information from free text health records when supported by RAG. To this end, we presented a flexible evaluation framework that automatically generates synthetic question and answer pairs tailored to the characteristics of each dataset or task. Experiments were conducted on a curated subset of MIMIC III, (four structured tables and one clinical note type), using a mix of locally hosted and API-based LLMs. Evaluation combined exact-match metrics, semantic similarity, and human judgment. Our findings demonstrate the potential of LLMs to support precise querying and accurate information extraction in clinical workflows.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 0 of its 1 references that are in the map. First posted on arXiv 28 Jan 2026.
Builds on
0 of 1 references.
No references here yet.
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Leveraging LLMs for Structured Data Extraction from Unstructured Patient Records
- 2024A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences
- 2025Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond
- 2025Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines
- 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
- 2025Retrieval-Augmented Framework for LLM-Based Clinical Decision Support
- 2026A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
- 2023MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering