Natural Language Processing to Improve Optimal Customized Treatment in Clinical Decision Support Systems

Through computational methods and natural language processing, sentiment analysis recognizes and classifies the sentiments conveyed by personal statements. The current effort aims to apply entity semantic analysis to construct a test case for a healthcare system for individualized medication. To assess if a patient's response to a cure, service, therapy, etc., is positive, negative, or neutral pharmaceutical data analysis is used. After a more thorough investigation of the patient surveys and an excellent diagnostic decision system, the gathered orientations were compared. To classify the pharmaceutical data analysis and contrast, sentiment polarity methods based on machine learning are also applied. Support vector machines (SVM), random forest classification, linear SVC, and multinomial NB are the most used analytic Sklearn techniques. It has been discovered that the SVM method achieves better accuracy than other algorithms.