Applicability Condition Extraction for Therapeutic Drug-Disease Relations

2026-06-11

AI Summary

This study addresses a critical limitation in existing biomedical information extraction methods, which often overlook the contextual conditions under which drug–disease treatment relationships hold, thereby hindering their utility for precise clinical decision-making. To bridge this gap, the work formally defines and implements the task of extracting such applicability conditions, introducing the first high-quality dataset comprising 1,119 drug–disease pairs annotated with human-labeled condition triplets. Furthermore, it proposes an enhanced LoRA (Low-Rank Adaptation) approach that integrates drug–disease relational knowledge through parameter-efficient fine-tuning to improve extraction performance. Experimental results demonstrate that the proposed method significantly outperforms strong baseline models across multiple evaluation settings. Both the dataset and source code have been made publicly available to support future research.

Abstract

Identifying conditions that a certain drug takes therapeutic effect on a target disease is crucial for clinical decision-making support. However, most existing biomedical information extraction methods have focused on identifying only relations between drugs and diseases, while largely overlooking the context-specific conditions where such relations can apply. To address this problem, we introduce the task of applicability condition extraction for therapeutic drug--disease relations from biomedical research literature. We create the first dataset that has manually annotated triples of drugs, diseases, and applicability conditions on biomedical paper abstracts with 1,119 drug-disease pairs. Using this dataset, we systematically evaluate the performance of a range of existing methods. In addition, we propose a new method that enhances LoRA to consider relations between drugs and diseases. Our method consistently outperforms strong baselines across different evaluation settings. The source code and dataset of this paper can be obtained from: https://github.com/guantingluo98/Drug-ACE

Problem

• applicability condition
• therapeutic drug-disease relations
• biomedical information extraction
• clinical decision support

Innovation

• applicability condition extraction
• therapeutic drug-disease relations
• LoRA enhancement
• biomedical information extraction
• annotated dataset