Leveraging Machine Learning and Patient Reviews for Developing a Drug Recommendation System to Reduce Medical Errors

Abstract

Background In the rapidly evolving pharmaceutical industry, drug efficacy and safety stand as critical concerns. The vast accumulation of data, including customer feedback, drug popularity, and usage details, offers a rich resource for improving healthcare outcomes. Aims The primary aim of this study is to harness machine learning and Natural Language Processing (NLP) techniques to sift through extensive pharmaceutical data, identifying the most effective drugs for various conditions and uncovering patterns that could guide better decision-making in drug efficacy and safety. Objective This research seeks to construct a sophisticated model capable of analyzing diverse data points to pinpoint the most efficacious drugs for specific health conditions, thereby providing pharmaceutical companies with data-driven insights to optimize drug safety and effectiveness. Methods Employing a blend of Natural Language Processing (NLP) and machine learning strategies, the study analyzes a comprehensive dataset featuring customer reviews, drug popularity metrics, usage information, and other relevant data collected over an extended period. This methodological approach aims to reveal latent trends and patterns that are crucial for assessing drug performance. Results The developed model adeptly identifies leading medications for various conditions, elucidating efficacy and safety profiles derived from patient reviews and drug utilization trends. These findings furnish pharmaceutical companies with actionable intelligence for enhancing drug development and patient care strategies. Conclusion The integration of machine learning and NLP for the analysis of vast drug-related datasets presents a powerful method for advancing drug efficacy and safety. The insights yielded by the proposed model significantly empower the decision-making processes of the pharmaceutical industry, ultimately fostering improved health outcomes for patients.