Drug Analysis System Using Machine Learning Algorithms

Internet-based healthcare communities persist in providing an extensive array of valuable medical insights, serving medical professionals, system administrators, and patients alike. Our approach involves the real-time compilation of health-related posts from reputable online sources, where patients share their perspectives, encompassing their encounters and potential drug side-effects. We intend to execute user post summarization on a per-drug basis, facilitating succinct findings accessible to both the medical community and patient cohort instantaneously. Further, we propose to classify the users based on their `emotional state of mind’. Furthermore, our methodology encompasses knowledge exploration within user-generated content, facilitating the identification of valuable ‘patterns’ pertaining to the interconnected ‘drugs-symptoms-medicine’ triad through the application of Association Learning techniques.