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Updated: Mar 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Forecasting-based biomedical time-series data synthesis for open data and robust AI.

Youngjoon Lee¹, Seongmin Cho¹, Yehhyun Jo¹

¹School of Electrical Engineering, KAIST, Daejeon, Republic of Korea.

Computers in Biology and Medicine

|March 7, 2026

Summary

This summary is machine-generated.

Generating synthetic biomedical time-series data using forecasting models overcomes data limitations for AI development. This approach enhances downstream model performance, achieving high accuracy even with synthetic data alone.

Keywords:

Biomedical AI Open-source data Synthetic data Time-series forecasting model

Related Experiment Videos

Last Updated: Mar 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Biomedical data science
Artificial intelligence in healthcare
Time-series analysis

Background:

Biomedical time-series AI development is hindered by data scarcity due to privacy regulations and resource intensity.
A gap exists between the data needs of AI models and the accessibility of real-world biomedical data.
Existing synthetic data methods like GANs and VAEs capture global distributions but may not be optimal for sequential data.

Purpose of the Study:

To propose a novel framework for generating synthetic biomedical time-series data.
To leverage advanced forecasting models for high-fidelity replication of complex electrophysiological signals (EEG, EMG).
To enable open AI development by providing shareable, privacy-preserving synthetic datasets.

Main Methods:

Developed a synthetic data generation framework utilizing recent forecasting models.
Focused on capturing the sequential dynamics inherent in biomedical time-series data.
Validated the framework on electroencephalography (EEG) and electromyography (EMG) signal generation.

Main Results:

The proposed framework accurately replicates complex electrophysiological signals with high fidelity.
Synthetic data augmentation improved downstream sleep-stage classification performance by up to 3.71%.
A model trained solely on synthetic data achieved 91.00% accuracy, outperforming a real-data-only baseline.

Conclusions:

Forecasting models provide an effective approach for generating high-fidelity synthetic biomedical time-series data.
Synthetic data generated by this framework can significantly enhance AI model performance and facilitate open research.
This method addresses data accessibility challenges while preserving patient confidentiality.