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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

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Published on: November 22, 2019

A new AI-assisted data standard accelerates interoperability in biomedical research.

Rodney Alan Long^1,2, Shannon Ballard^1,2, Syed Shah^1,2

¹Center for Alzheimer's and Related Dementias, National Institute on Aging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.

Medrxiv : the Preprint Server for Health Sciences

|November 1, 2024

Summary

This summary is machine-generated.

Large Language Models (LLMs) automate biomedical data harmonization by generating Common Data Elements (CDEs), improving data discovery and AI-readiness. This accelerates research and enhances dataset interoperability.

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Published on: August 4, 2023

Area of Science:

Biomedical Informatics
Artificial Intelligence in Healthcare
Data Science

Background:

Data harmonization is critical for biomedical research but is labor-intensive and hinders data discovery.
Lack of standardized data elements impedes interoperability and AI-readiness across diverse datasets.
Automating data wrangling and harmonization is essential for efficient and scalable biomedical research.

Purpose of the Study:

To leverage Large Language Models (LLMs) for automating the generation of Common Data Elements (CDEs).
To enhance data discovery, promote interoperability standards, and facilitate AI-readiness in biomedical science.
To develop a scalable and efficient method for harmonizing multiple biomedical datasets.

Main Methods:

Utilized 31 studies, ontologies, and medical coding systems to generate CDEs.
Employed 4th-generation OpenAI GPT models via API requests to populate metadata fields.
Implemented a human-in-the-loop (HITL) approach for quality assessment and utilized ElasticSearch to manage CDE generation and prevent duplicates.

Main Results:

94.0% of generated metadata fields required no manual revision by subject matter experts.
Successfully mapped dataset column headers to generated CDEs at a 32.4% rate using ElasticSearch.
Achieved an average interoperability score of 53.8 out of 100 for test datasets (ADNI and GP2).

Conclusions:

LLM-driven CDE generation significantly accelerates data harmonization and improves data quality.
The developed approach enhances dataset interoperability and facilitates AI-readiness in biomedical research.
Automating tedious data wrangling tasks reduces activation energy for federated research and increases efficiency.