Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Developing a natural language processing system using transformer-based models for adverse drug event detection in electronic health records.

PloS one·2026

Same author

Cardiovascular Disease Risk and Noncardiovascular Chronic Disease Burden by Housing Status.

Journal of the American Heart Association·2026

Same author

The Rise in Artificial Intelligence and Machine Learning Models to Screen for Cleft-Related Velopharyngeal Dysfunction: A Systematic Review.

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association·2026

Same author

Advancing in-hospital mortality prediction for acute myocardial infarction: An analysis from the American Heart Association Get With The Guidelines-Coronary Artery Disease Registry.

American heart journal·2026

Same author

The Burden and Risk Factors of Colorectal Cancer in Older Adults From 2010 to 2021: A Systematic Analysis for the Global Burden of Disease Study 2021.

Clinical and translational gastroenterology·2026

Same author

Correction to: Pragmatic Approaches to the Evaluation and Monitoring of Artificial Intelligence in Health Care: A Science Advisory From the American Heart Association.

Circulation·2026

Same journal

LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same journal

Evaluating Representation Embeddings from LLMs and Time-Series Foundation Models for Wearable Accelerometer-Based Health Prediction.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same journal

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same journal

Mapping the Storm: Linking Tornado Paths to Emergency Room Surges Through Geocoded Patient Data.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same journal

Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same journal

A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Optimizing an LLM-Based Clinical Data Querying System Using Metadata Enrichment and Task Decomposition.

Weixin Liu¹, Bowen Qu¹, Pratheek Mallya²

¹Vanderbilt University, Nashville, TN, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|June 19, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) enable natural language querying of clinical registries, improving data accessibility for researchers. This text-to-SQL solution enhances accuracy, especially for complex queries, while maintaining privacy.

More Related Videos

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 20, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

Health Informatics
Artificial Intelligence in Medicine
Data Science

Background:

Clinical registries are valuable data sources but require SQL expertise for access.
Non-technical researchers face significant barriers to querying complex clinical data.

Purpose of the Study:

To evaluate a text-to-SQL solution using large language models (LLMs) for natural language querying of clinical registries.
To assess the feasibility of LLM-based data access under strict privacy and security constraints.

Main Methods:

Developed a multi-layered optimization framework for self-hosted, open-source LLMs.
Incorporated metadata enrichment, query decomposition, hybrid retrieval, and SQL self-correction.
Validated performance on 600 queries of varying complexity (one-, two-, and three-field).

Main Results:

Improved accuracy from 88.0% to 94.5% for one-field queries.
Significantly enhanced accuracy from 10.0% to 82.0% for three-field queries.
Identified domain-specific challenges in real-world testing, including coded variables and clinical ambiguity.

Conclusions:

LLM-based text-to-SQL solutions can significantly improve clinical registry data accessibility.
Addressing domain-specific challenges is crucial for safe and scalable deployment of LLM tools in healthcare.
Further research is needed to optimize LLM performance for complex clinical data analysis.