Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning

Abstract

Since coronavirus has shown up, inaccessibility of legitimate clinical resources is at its peak, like the shortage of specialists and healthcare workers, lack of proper equipment and medicines etc. The entire medical fraternity is in distress, which results in numerous individual's demise. Due to unavailability, individuals started taking medication independently without appropriate consultation, making the health condition worse than usual. As of late, machine learning has been valuable in numerous applications, and there is an increase in innovative work for automation. This paper intends to present a drug recommender system that can drastically reduce specialists heap. In this research, we build a medicine recommendation system that uses patient reviews to predict the sentiment using various vectorization processes like Bow, TF-IDF, Word2Vec, and Manual Feature Analysis, which can help recommend the top drug for a given disease by different classification algorithms. The predicted sentiments were evaluated by precision, recall, f1score, accuracy, and AUC score. The results show that classifier LinearSVC using TF-IDF vectorization outperforms all other models with 93% accuracy.

SeEn: Sequential enriched datasets for sequence-aware recommendations

“…An investigator studying the effect of chemical compounds in the creation of new drugs would be more than glad if a system recommended the next best match to their studies preferences. Several recent studies support this claim [1][2][3][4][5] . The goal of most of these studies is to obtain a recommendation for the response a drug will have in the patients, for example, realizing studies with patients' cell cultures [1][2][3][4] .…”

“…The goal of most of these studies is to obtain a recommendation for the response a drug will have in the patients, for example, realizing studies with patients' cell cultures [1][2][3][4] . A different approach was followed by 5 , in which the recommendations are based on sentiment analyses of the patient's reviews about drugs. Other scientific fields, such as Health 6 , and Astronomy [7][8][9] , are following the trend in seeking recommendations for finding new items of interest.…”

Extraction of Drug Review Polarity Using Sentimental Analysis

“…The aspect level based method is presented in соmрiling drug review dаtа frоm three аnnоtаtоrs tо be аnd be рushed fоr рrосessing оver multiрle rоunds. Аfter getting reаsоnаble аgreement frоm these аnnоtаtоrs, the соlleсted dаtа will be sentimentаlly аnаlyzed оver multiрle lаyers, namely Embedded lаyer, DBIGRU lаyer, РM-DBIGRU lаyer, Аttentiоn lаyer, Sоftmаx lаyer tо determine its роlаrity аmоng its соnsumers [1]. DRАW-Drug Review Аnаlysis Wоrk, this reseаrсh рарer is рrimаrily fосused оn соnverting rаw text dаtа intо useful numeriс reрresentаtiоn sinсe ML аlgоrithms саnnоt рrосess rаw text dаtа.…”

Design of a Diagnostic System for Patient Recovery Based on Deep Learning Image Processing: For the Prevention of Bedsores and Leg Rehabilitation

“…Another study was conducted to confirm the importance of leg muscle strength and to provide rehabilitation programs that can be performed at home for patients with weak leg muscle strength using an online system. Methods have also been studied using related robotics and IoT technologies, and various attempts are being made to solve some of the rehabilitation-related processes that do not have to be performed by medical personnel or in hospitals [ 40 , 41 , 42 , 43 ]. Patients rely on rehabilitation treatment to return to their daily lives, and rehabilitation through exercise is an essential factor in medical practice because it provides various solutions along with medication.…”