MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202641085501 A) filed by Malla Reddy Engineering College For Women Autonomous; Malla Reddy University; Malla Reddy Mr Deemed To Be University; and Malla Reddy Vishwavidyapeeth Deemed on July 13, 2026, for Drug Recommendation System Based On Sentiment Analysis Of Drug Reviews.
Inventors include Dr. Y. Madhaveelatha; Dr. Srikanth Thurimella; Mr. Kumar Manish Sinha; Ms. Thotakoora Swathi; Ms. Naga Lakshmi Panchakatla; Dr. T. Sanjeeva Rao; Mr. Rongala Ravi; and Mrs. Tanneru Venkata Lavanya.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: The Drug Recommendation System based on Sentiment Analysis of Drug Reviews is an intelligent healthcare support system that analyzes patient reviews and feedback about medications to recommend suitable drugs for specific medical conditions. In recent years, online healthcare platforms and medical forums have generated large amounts of patient- generated drug reviews describing experiences, effectiveness, and side effects of medicines. However, manually analyzing these reviews is difficult due to the large volume of textual data. The proposed system applies Natural Language Processing (NLP) and Machine Learning techniques to analyze drug reviews and determine the sentiment expressed by patients. The sentiment analysis process classifies reviews into categories such as positive, negative, or neutral. Based on these sentiments, the system evaluates the effectiveness and reliability of different medications. The system collects drug reviews from medical datasets or healthcare platforms and performs preprocessing steps such as text cleaning, tokenization, stop-word removal, and normalization. Machine learning models such as Naïve Bayes, Support Vector Machines, or deep learning models are then used to perform sentiment classification. After analyzing review sentiments, the system recommends drugs that have higher positive sentiment scores and better patient satisfaction for a given medical condition. The system also provides confidence scores and side-effect insights to assist patients and healthcare professionals in making informed decisions. The proposed approach helps improve healthcare decision support by utilizing patient experiences and medical data. It provides a data-driven recommendation system that enhances drug selection, improves patient safety, and supports personalized treatment recommendations.
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