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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)  
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.  ...  This work was supported in part by the National Institutes of Health grant 1R15LM011999-01.  ... 
doi:10.1109/bibm.2017.8217820 pmid:29977659 pmcid:PMC6029703 dblp:conf/bibm/CalixGGJ17 fatcat:zwmts7fhubdlrej2h2quge7nu4

Social Media based Surveillance Systems for Healthcare using Machine Learning: A Systematic Review

Aakansha Gupta, Rahul Katarya
2020 Journal of Biomedical Informatics  
Based on the corpus of 148 selected articles, the study finds the types of social media or web-based platforms used for surveillance in the healthcare domain, along with the health topic(s) studied by  ...  In this paper, we review the recent work, trends, and machine learning(ML) text classification approaches used by surveillance systems seeking social media data in the healthcare domain.  ...  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  ... 
doi:10.1016/j.jbi.2020.103500 pmid:32622833 pmcid:PMC7331523 fatcat:jfj34tsxkvhlhkwnna6fmgnvni

Identifying Personal Experience Tweets of Medication Effects Using Pre-trained RoBERTa Language Model and Its Updating

Minghao Zhu, Youzhe Song, Ge Jin, Keyuan Jiang
2020 Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis   unpublished
Identifying personal experience tweets is a challenging classification task in natural language processing.  ...  Our results show that all of these approaches outperform the published methods (Word Embedding + LSTM) in classification performance (p < 0.05), and updating the pre-trained language model with tweets  ...  Subsequently, Calix and colleagues introduced the concept of deep gramulator to include a textual feature that contains expressions in one class but not in the opposite class, to improve the discriminatory  ... 
doi:10.18653/v1/2020.louhi-1.14 fatcat:ebnc4awqrbaadanm6u6tveg6re