Certain investigations on drug recommendations using machine learning techniques

Abstract

Sentiment Analysis is regarded as the crown of Natural Language Processing. Analysing and understanding users opinions and their reviews are a challenging tasks. In the modern times, user s opinions about a particular product or reviews of a movie are available in social media such as Facebook and Twitter. Health related discussions are also available in online health community platforms such as DailyStrength, Medhelp, PatientsLikeMe, etc. newlineAdverse Drug Reaction (ADR) is defined as the serious side effects on the human bodies due to the indiscriminate usage of medicines without the doctors prescription. Automatic detection of Adverse Drug Reaction (ADR) from social media content is a challenging research problem that has received significant attention in the realm of Pharmacovigilance. Massive amount of data discussion in social media is a useful resource for ADR. Hence, efficient machine learning techniques are needed to address the informal vocabulary and misspellings used in social media. newlineThe objective of this research is to develop deep learning models to detect and improve the performance of ADR. Many methodologies and algorithms have been proposed to detect ADR about the drugs. However, this research study proposes a more efficient and accurate framework, namely Adverse Drug Effect Aware Recommendation System to detect ADR. This work focuses on reviews collected from the Twitter social media about to a particular drug reaction. These reviews are analysed to classify the reactions of users into positive or negative, based on the adverse effects newline