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MTMG: A multi-task model with multi-granularity information for drug-drug interaction extraction.

Haohan Deng1, Qiaoqin Li1, Yongguo Liu1

  • 1Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.

Heliyon
|July 24, 2023
PubMed
Summary

This study introduces MTMG, a novel framework for drug-drug interaction (DDI) extraction. MTMG improves accuracy by jointly training drug named entity recognition and DDI classification, outperforming existing methods.

Keywords:
Drug named entity recognitionDrug-drug interactionsMulti-granularityMulti-task framework

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Area of Science:

  • Biomedical Informatics
  • Computational Linguistics
  • Pharmacovigilance

Background:

  • Manual extraction of drug-drug interactions (DDIs) from biomedical texts is time-consuming and costly.
  • Automated DDI extraction is crucial for patient safety, understanding drug mechanisms, and optimizing combination therapies.
  • Existing neural network methods often rely on pre-identified drug entities, leading to error propagation and limited real-world applicability.

Purpose of the Study:

  • To develop a novel multi-task framework, MTMG, for improved drug-drug interaction (DDI) extraction.
  • To shift DDI extraction from a sentence-level classification to a sequence labeling task (Drug-Specified Token Classification - DSTC).
  • To enhance the overall DDI extraction pipeline by integrating drug named entity recognition (DNER) and auxiliary tasks.

Main Methods:

  • Proposed a multi-task learning framework (MTMG) for joint training of DSTC and DNER.
  • Re-framed DDI extraction as a sequence labeling task (DSTC).
  • Incorporated two designed sentence-level auxiliary tasks to leverage correlations and varying data granularity.

Main Results:

  • MTMG significantly improved the accuracy of both DNER and DDI extraction.
  • The proposed framework demonstrated superior performance compared to state-of-the-art techniques.
  • The multi-task approach effectively addressed error propagation issues inherent in previous methods.

Conclusions:

  • The MTMG framework offers a more robust and accurate approach to automated DDI extraction.
  • Jointly learning DNER and DSTC within a multi-task framework enhances pipeline universality and performance.
  • This method holds promise for advancing pharmacovigilance and clinical decision support systems.