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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Facilitating Clinical Information Extraction with Synthetic Data and Ontology using Large Language Models.

Yan Hu¹, Huan He², Qingyu Chen²

¹Mcwilliam School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|February 23, 2026

Summary

Large language models can generate synthetic clinical data to improve named entity recognition. Self-verification and semantic mapping enhance data utility, with a 1:1 ratio of human to synthetic data optimizing performance.

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

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Published on: September 20, 2018

Area of Science:

Natural Language Processing
Artificial Intelligence in Healthcare

Background:

Electronic health records contain vast unstructured clinical text.
Developing information extraction systems is crucial but limited by scarce annotated data.

Purpose of the Study:

To explore large language models for generating synthetic clinical data for named entity recognition.
To assess the impact of synthetic data on model performance and generalizability.

Main Methods:

A novel framework using self-verified synthetic data generation with SNOMED-CT semantic mapping.
Leveraging GPT-4o-mini for data creation and LLaMA-3-8B for fine-tuning.
Iterative verification and anomaly detection to refine synthetic data quality.

Main Results:

Self-verification and semantic mapping significantly improve synthetic data utility.
A 1:1 ratio of human-annotated to synthetic data yielded optimal performance gains.
Improved model generalizability observed across four diverse clinical datasets.

Conclusions:

Synthetic data generation is a scalable solution for clinical NLP annotation challenges.
Balancing human and synthetic data is key to enhancing model performance.
The proposed framework advances clinical information extraction capabilities.