Prediction of Personal Experience Tweets of Medication Use via Contextual Word Representations.

Clicks: 218
ID: 94072
2019
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Abstract
Continuous monitoring the safe use of medication is an important task in pharmacovigilance. The first-hand experiences of medication effects come from the consumers of the pharmaceuticals. Social media have been considered as a possible alternative data source for gathering consumer-generated information of their experience with medications. Identifying personal experience in social media data is a challenging task in natural language processing. In this study, we investigated a method of predicating personal experience tweets using Google's Bidirectional Encoder Representations from Transformers (BERT) and neural networks, in which BERT models contextually represented the tweet text. Both pre-trained BERT models and our BERT model trained with 3.2 million unlabeled tweets were examined. Our results show that our trained BERT model performs better than Google's pre-trained models (p <; 0.01). This suggests that domain-specific data may contribute to the BERT model yielding better classification performance in predicting personal experience tweets of medication use.
Reference Key
jiang2019predictionconference Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jiang, Keyuan;Chen, Tingyu;Calix, Ricardo A;Bernard, Gordon R;
Journal conference proceedings : annual international conference of the ieee engineering in medicine and biology society ieee engineering in medicine and biology society annual conference
Year 2019
DOI
10.1109/EMBC.2019.8856753
URL
Keywords
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