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Predicting drug interactions from biomedical literature is important for ensuring patient safety and guiding medical practitioners. In this paper, we propose an NLP-based approach using a multi-class support vector machine (SVM) model with a linear kernel and balanced class weights. By utilizing the abstracts, titles of publications, and the sections of the methodology, the models used classifies on three levels of hierarchy. The accuracy and metrics performance achieved are the results and proofs to show that the model is efficient. Based on this, our method is an important tool in boosting healthcare efficiency.

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A Hierarchical Approach to Drug Interaction Prediction Using Natural Language Processing and Support Vector Machines

  • 会议论文

  • 发表日期：

    2026年

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  • M. Laxman Rao,
  • Gudditi Chetan,
  • Kotha Lavanya,
  • S. B. S. S. S. Vamsi Krishna,
  • Beebi Naseeba,
  • S. Karthikeyan

摘要

Predicting drug interactions from biomedical literature is important for ensuring patient safety and guiding medical practitioners. In this paper, we propose an NLP-based approach using a multi-class support vector machine (SVM) model with a linear kernel and balanced class weights. By utilizing the abstracts, titles of publications, and the sections of the methodology, the models used classifies on three levels of hierarchy. The accuracy and metrics performance achieved are the results and proofs to show that the model is efficient. Based on this, our method is an important tool in boosting healthcare efficiency.