首页|Variations towards an efficient drug-drug interaction

Variations towards an efficient drug-drug interaction

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  • NETL
  • NSTL
  • Oxford Univ Press
Drug-drug interactions (DDIs) are a crucial research focus in clinical pharmacology and public health. DDIs can lead to reduced drug efficacy or increased adverse reactions, making the effective identification and understanding of drug interactions essential for patient safety and treatment outcomes. With the rapid growth of biomedical literature, automated methods for extracting DDI information have become increasingly necessary. In this paper, we propose BLRG, a novel model that uniquely integrates BioBERT, long short-term memory (LSTM), and relational graph convolutional network (R-GCN) to extract complex DDIs. This combination allows the model to effectively capture both semantic and relational features, outperforming existing methods in handling intricate dependencies in biomedical texts. Specifically, our approach begins by utilizing the BioBERT model to capture deep contextual features of sentences, extracting their semantic information. Following this, an LSTM network processes the sequential features of the sentence to model its contextual dependencies. Finally, an R-GCN is applied to identify and interpret the relationships between drug entities within the sentence, accurately capturing DDI information. Experimental results demonstrate that our model significantly outperforms current state-of-the-art methods across standard datasets, showcasing its effectiveness and potential in complex DDI extraction tasks.

Yaxun Jia、Zhu Yuan、Haoyang Wang、Yunchao Gong、Haixiang Yang、Zuo-lin Xiang

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Department of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China

Department of Information Management, The National Police University for Criminal Justice, Baoding 071000, China

Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China

Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China||Computer College, Qinghai Normal University, Xining 810008, China

Big Data Center of the Ministry of Public Security, Beijing 100070, China

Department of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China||Department of Radiation Oncology, Shanghai East Hospital Ji'an hospital, Jian 343006, China

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2025

10.1093/comjnl/bxae131

The computer journal

The computer journal

ISSN：0010-4620
年,卷(期)：2025.68(5)
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