A transfer learning approach for adverse drug reactions detection in bio-medical domain based on knowledge graph Online publication date: Mon, 03-Mar-2025
by Monika Yadav; Prachi Ahlawat; Vijendra Singh
International Journal of Computational Science and Engineering (IJCSE), Vol. 28, No. 2, 2025
Abstract: Among the top causes of mortality, adverse reactions to drugs (ADRs) are dominant. This imposes severe health risks and a significant financial burden on patients. Consequently, timely prediction of possible ADRs of a drug has become an essential concern in the clinical domain. However, it is challenging to recognise the adverse reactions of all drugs using existing ADR data sources. Recently, a semantic-rich knowledge base and machine learning techniques have shown high accuracy in predicting ADRs. This paper introduces a new framework, knowledge graph slot-filling clinical bi-directional encoder representations from transformers (KG-SF Clinical BERT), which takes triples of knowledge graph as text sequences. It applies transformer-based multi-task learning with slot-filling for ADR classification and fine-tuned on bio-medical domain to detect ADRs. The KG-SF Clinical BERT brings remarkable performance gain with AUC of 0.88 on drug bank and SIDER datasets and 0.99 AUC on PubMed dataset.
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