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This research introduces a novel NLP task focused on extracting context-specific applicability conditions for drug-disease relations from biomedical literature. By enhancing LoRA with this new dataset covering over 1,000 pairs, the study significantly improves upon existing methods that typically ignore contextual constraints in clinical decision support systems. The work represents a meaningful advancement in specialized AI applications within bioinformatics and precision medicine.
Applicability Condition Extraction for Therapeutic Drug-Disease Relations
arXiv:2606.14031v1 Announce Type: new 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
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: this https URL
Artificial Intelligence (cs.AI)
arXiv:2606.14031 [cs.AI]
(or arXiv:2606.14031v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.14031 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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