Evaluating the Effectiveness of Medication Across Diseases

The method to improve healthcare outcomes and to treat all diseases, from acute bacterial sinusitis and adenocarcinoma of the pancreas, and all the way to atopic dermatitis, is through medication effectiveness assessment. This work develops a proposal for a patient review-based machine learning model that evaluates the efficacy of medications for 37 commonly treated medical conditions. A general shortcoming in drug performance algorithms is the fact that they often rely purely on clinical trials or opinions of drug experts, disregarding real user feedback within the world itself. While these have, to a great extent, proved to be very instrumental in the advancement of medical science, they always miss patient-centric factors such as ease of use and satisfaction-mostly critical for long-term effectiveness of medication and adherence. The study bridges some gaps with the analysis of a dataset including drug names, types, average price, reviews, and performance metrics such as effectiveness, ease of use, and satisfaction. Our model summarizes these data to forecast which drugs would best suit a given condition based on user experience. Our proposed model provides for an overall better medication evaluation by applying ML algorithms to user reviews. It effectively closes the gap between outcomes reported by patients and clinical efficacy. Such research would help prescribing officers provide the most efficacious medicine to patients, thereby reducing their workload and helping in better care of the patients.