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The global healthcare system is under pressure with shortages of resources, a lack of prompt medical consultation, increased self-medication, etc. In order to solve these problems, we proposed a Deep Learning-Based Drug Recommendation System to recommend appropriate medications. The structure rapidly forecasts suggestions from opinion mapping with a state-of-the-art text vectorization strategy like TF - IDF, GLOVE, with deep learning based classification models. We calculated the Accuracy, Precision, Recall and F1-score for the predicted sentiments. Our proposed Convolutional Neural Network (CNN) significantly outperformed an initial SVM with accuracy of (CNN) 97%, whereas SVM only managed 86%. This indicates how well deep learning model works to select the best method for treatment. Future drug discovery could use this strategy to tailor drug selections for specific diseases.

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A Deep Learning Driven Drug Recommendation System Using Patient Sentiment Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • N. Venkata Sailaja,
  • K. Himasree,
  • Boddapalli Vamsi Krishna

摘要

The global healthcare system is under pressure with shortages of resources, a lack of prompt medical consultation, increased self-medication, etc. In order to solve these problems, we proposed a Deep Learning-Based Drug Recommendation System to recommend appropriate medications. The structure rapidly forecasts suggestions from opinion mapping with a state-of-the-art text vectorization strategy like TF - IDF, GLOVE, with deep learning based classification models. We calculated the Accuracy, Precision, Recall and F1-score for the predicted sentiments. Our proposed Convolutional Neural Network (CNN) significantly outperformed an initial SVM with accuracy of (CNN) 97%, whereas SVM only managed 86%. This indicates how well deep learning model works to select the best method for treatment. Future drug discovery could use this strategy to tailor drug selections for specific diseases.