Analyzing medical images to detect adverse drug reactions and toxicity
DOI:
https://doi.org/10.52783/jns.v14.1571Keywords:
Pharmacovigilance, ADR, General structure, PVAbstract
It is believed that sentiment analysis is the pinnacle of natural language processing. It can be difficult to analyze and comprehend customer reviews and comments. These days, social media sites like Facebook and Twitter allow users to share their thoughts about a product or movie. Online health community platforms like Daily Strength, Me Help, Patients Like Me, and others also offer health-related conversations. Serious negative effects on human bodies brought on by careless medication use without a prescription are known as adverse drug reactions, or ADRs. In the field of pharmacovigilance, the difficult research problem of automatically detecting adverse drug reactions (ADRs) from social media information has drawn a lot of attention. ADR can benefit from the vast amount of data discussion on social media. Therefore, to address the casual vocabulary and misspellings prevalent in social media, effective machine learning approaches are required. Creating deep learning models to identify and enhance ADR performance is the aim of this study. Numerous approaches and algorithms have been put forth to identify adverse medication reactions. To detect ADR, this research study suggests a more accurate and efficient framework called the "Adverse Drug Effect Aware Recommendation System." This study focuses on reviews of a specific drug reaction that were gathered from Twitter.
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