PHARMA SENTINEL: SENTIMENT-DRIVEN NLP DRUG SUGGESTION SYSTEM

Abstract

This study investigates the use of sentiment analysis and natural language processing (NLP) approaches in the field of drug recommendation in order to improve the accuracy and effectiveness of patients' medicine selection. The system is based on a quantitative approach, which combines data mining, sentiment analysis, and machine learning techniques. The sentiment analysis algorithm is able to identify subtle emotional differences in patient encounters by analyzing unstructured medical texts such as electronic health records and patient reviews. The NLP algorithm can also uncover hidden attitudes, worries, and sentiments regarding drugs and treatment plans through analyzing the sentiment and tone in patient interactions. The algorithm can be used to classify medical tests using a variety of classification methods, such as XGBoost, Passive Aggressive Classifier, and Multinomial Nave Bayes. The performance of the algorithm is evaluated by comparing the accuracy, precision.