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

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CAS: enhancing implicit constrained data augmentation with semantic enrichment for biomedical relation extraction and

Fang-Yi Su¹, Gia-Han Ngo¹, Ben Phan¹

¹Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City 701401, Taiwan.

Database : the Journal of Biological Databases and Curation

|July 3, 2025

Summary

This summary is machine-generated.

Constrained Augmentation and Semantic-Quality (CAS) enhances data augmentation for constrained datasets by using large language models to generate rule-adherent variations. This framework ensures data integrity and improves model performance in domains like biomedical NLP.

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Area of Science:

Natural Language Processing
Computational Biology
Data Science

Background:

Biomedical relation extraction datasets often have implicit constraints crucial for data integrity.
Traditional data augmentation methods risk violating these domain-specific rules.
Existing techniques are insufficient for augmenting data in constrained environments.

Purpose of the Study:

To introduce a novel framework, Constrained Augmentation and Semantic-Quality (CAS), for data augmentation in constrained datasets.
To address the limitations of traditional augmentation methods in preserving data integrity.
To improve model performance on tasks with implicit constraints.

Main Methods:

CAS utilizes large language models to generate diverse data variations.
The framework incorporates a SemQ Filter for self-evaluation and quality control.
It ensures augmented data adheres to predefined structural, syntactic, or semantic rules.

Main Results:

CAS successfully generates high-quality, semantically consistent augmented data.
The framework maintains structural fidelity and semantic accuracy.
Experiments show enhanced model performance across multiple domains using CAS.

Conclusions:

CAS offers a robust solution for data augmentation in constrained datasets, particularly in biomedical NLP.
The framework's versatility extends its application to other NLP tasks with implicit constraints.
CAS advances the field by enabling reliable data augmentation while preserving essential data integrity.