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In this work, we investigate the use of Natural Language Processing (NLP) approaches to categorize patient conditions based on drug reviews. We use a dataset from Drugs.com to focus on four particular conditions: birth control, depression, high blood pressure, and type 2 diabetes. The approach involves data preprocessing, feature extraction with TF-IDF, and the use of machine learning classifiers like Multinomial Naive Bayes and Passive Aggressive Classifier. Our findings show that natural language processing has the capacity to effectively diagnose medical situations, which can considerably improve patient care, drug effectiveness monitoring, and healthcare research.

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Patient’s Condition Classification Using Drug Reviews with NLP

  • 会议论文

  • 发表日期：

    2025年

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  • Victor Doma,
  • Erna Berbić,
  • Ali Abd Almisreb,
  • Muhammad Asraf Hairuddin,
  • Binara Imankulova

摘要

In this work, we investigate the use of Natural Language Processing (NLP) approaches to categorize patient conditions based on drug reviews. We use a dataset from Drugs.com to focus on four particular conditions: birth control, depression, high blood pressure, and type 2 diabetes. The approach involves data preprocessing, feature extraction with TF-IDF, and the use of machine learning classifiers like Multinomial Naive Bayes and Passive Aggressive Classifier. Our findings show that natural language processing has the capacity to effectively diagnose medical situations, which can considerably improve patient care, drug effectiveness monitoring, and healthcare research.