QAMT: An LLM-Based Framework for Quality-Assured Medical Time-Series Data Generation | JoVE Visualize

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Data Science

Background:

Real-world medical time-series data are crucial for research and clinical decisions but face challenges like limited volume, poor quality, and privacy concerns.
Existing data generation methods, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), struggle with the complexity of medical data, particularly static event data, and often lack interpretability.
Large language models (LLMs) show promise but face difficulties in generating temporal data and domain-specific nuances.

Purpose of the Study:

To propose QAMT, the first LLM-based framework for modularly generating quality-assured and interpretable medical time-series data.
To overcome the limitations of existing methods in generating high-quality static and temporal medical data.
To enhance the utility of synthetic medical data for downstream tasks like medical research and clinical decision-making.

Main Methods:

Developed QAMT, a modular framework leveraging LLMs for medical time-series data generation.
Constructed a health knowledge graph to imbue LLMs with medical expertise.
Designed dual modules for simultaneous generation of static event and temporal data, incorporating a quality assurance module.

Main Results:

QAMT successfully generates medical time-series data with improved quality compared to existing methods.
The framework ensures the interpretability of the data generation process, a key advantage over traditional approaches.
Experimental results validate the effectiveness of QAMT in producing reliable synthetic medical data.

Conclusions:

QAMT represents a significant advancement in generating high-quality, interpretable medical time-series data using LLMs.
The modular design and integration of a knowledge graph and quality assurance module address critical challenges in synthetic medical data generation.
QAMT offers a promising solution for augmenting real-world medical data, thereby supporting advancements in medical research and clinical practice.