DREaM: Drug-Drug Relation Extraction via Transfer Learning Method

TL;DR

This paper introduces DREaM, a transfer learning-based method for extracting drug-drug relations from medical texts, leveraging large language models for validation, and addressing the scarcity of specialized datasets.

Contribution

DREaM is a novel transfer learning approach that constructs a drug relation ontology from medical texts and validates relations using large language models.

Findings

LLM agreed with 71 relations from PubMed abstracts

Method uncovers ambiguities in medical relation extraction

Addresses dataset scarcity in drug relation extraction

Abstract

Relation extraction between drugs plays a crucial role in identifying drug drug interactions and predicting side effects. The advancement of machine learning methods in relation extraction, along with the development of large medical text databases, has enabled the low cost extraction of such relations compared to other approaches that typically require expert knowledge. However, to the best of our knowledge, there are limited datasets specifically designed for drug drug relation extraction currently available. Therefore, employing transfer learning becomes necessary to apply machine learning methods in this domain. In this study, we propose DREAM, a method that first employs a trained relation extraction model to discover relations between entities and then applies this model to a corpus of medical texts to construct an ontology of drug relationships. The extracted relations are subsequently validated using a large language model. Quantitative results indicate that the LLM agreed with 71 of the relations extracted from a subset of PubMed abstracts. Furthermore, our qualitative analysis indicates that this approach can uncover ambiguities in the medical domain, highlighting the challenges inherent in relation extraction in this field.