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To ensure safe and beneficial healthcare practices it is important to understand how drugs are used as well as their side effects, even though some knowledge about the former is found with people, but there still exists limited awareness regarding the latter. The research proposes a new method that utilizes CS-CNN to predict drug side effects based on drug names. The CS-CNN approach utilizes both Cosine Similarity and Convolutional Neural Network benefits. The CNN uses 2966 different drugs and their descriptions from the Sider dataset to make predictions of side effects. With an amazing prediction accuracy of 88%, the method makes accurate predictions in many cases. Through the research, it will be possible for individuals to know more about these side effects thus better preparing them for informed decision-making during healthcare practice.

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Drug Side Effects Prediction Using CS-CNN Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Anand Swaroop Inedi,
  • Lakshmikanth Paleti,
  • Srinivas Rao Mandalapu,
  • Nithin Reddy Devireddy,
  • Ramaswamynadhan Guntupalli

摘要

To ensure safe and beneficial healthcare practices it is important to understand how drugs are used as well as their side effects, even though some knowledge about the former is found with people, but there still exists limited awareness regarding the latter. The research proposes a new method that utilizes CS-CNN to predict drug side effects based on drug names. The CS-CNN approach utilizes both Cosine Similarity and Convolutional Neural Network benefits. The CNN uses 2966 different drugs and their descriptions from the Sider dataset to make predictions of side effects. With an amazing prediction accuracy of 88%, the method makes accurate predictions in many cases. Through the research, it will be possible for individuals to know more about these side effects thus better preparing them for informed decision-making during healthcare practice.