Deep gramulator: Improving precision in the classification of personal health-experience tweets with deep learning

Ricardo A. Calix, Ravish Gupta, Matrika Gupta, Keyuan Jiang
2017 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
Health surveillance is an important task to track the happenings related to human health, and one of its areas is pharmacovigilance. Pharmacovigilance tracks and monitors safe use of pharmaceutical products. Pharmacovigilance involves tracking side effects that may be caused by medicines and other health related drugs. Medical professionals have a difficult time collecting this information. It is anticipated that social media could help to collect this data and track side effects. Twitter data
more » ... an be used for this task given that users post their personal health related experiences on-line. One problem with Twitter data, however, is that it contains a lot of noise. Therefore, an approach is needed to remove the noise. In this paper, several machine learning algorithms including deep neural nets are used to build classifiers that can help to detect these Personal Experience Tweets (PETs). Finally, we propose a method called the Deep Gramulator that improves results. Results of the analysis are presented and discussed.
doi:10.1109/bibm.2017.8217820 pmid:29977659 pmcid:PMC6029703 dblp:conf/bibm/CalixGGJ17 fatcat:zwmts7fhubdlrej2h2quge7nu4