Machine learning-based recommendation system for disease-drug material and adverse drug reaction: Comparative review

Abstract

Recently, the social media podium used to share and post patient medical content, which significantly lead to mining medical content for pharmacovigilance, drug repositioning, and Healthcare sector. Paramedical advertisers extract trusted health information from the social media platform and correlate health care information with the user profile for recommending proper treatment. The mining of the patient's personal and medical information on social media makes the patient aware of the recent drug revolution. For predicting computational medical services, there is a need to Develop a centralized Social media-based pharmacovigilance activities virtual podium that identify drug and ADR associated with it. Build an informational community of homogenous E-patient over the virtual platform for building informational and emotional support more efficiently. In this paper, a comprehensive overview of the recommendation system and possible research gap associated with drug repository and ADR are presented based on recent research. Furthermore, this paper present clinical post pre-processing, clinical entity recognition, tweet pooling and expert rating framework to classify Disease-Drug-ADR intersection for recommending drug and ADR. Simultaneously this framework present comparative analysis of benchmark classifier to trained and label corpus of tweets for drug repositioning and ADR automatically.