#phramacovigilance - Exploring Deep Learning Techniques for Identifying Mentions of Medication Intake from Twitter

TL;DR

This paper investigates deep learning models, including a novel stacked CNN ensemble, for accurately identifying tweets that mention personal medication intake, advancing pharmacovigilance research at an individual level.

Contribution

It introduces a new stacked CNN ensemble architecture and demonstrates its effectiveness in detecting medication mentions in tweets, achieving state-of-the-art performance.

Findings

Achieved a micro-averaged F-score of 0.693

Compared multiple deep neural network models and optimized hyperparameters

Demonstrated the effectiveness of ensemble methods in social media text classification

Abstract

Mining social media messages for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions of drug usage and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific cohorts, identifying posts mentioning intake of medicine by the user is necessary. Towards this objective, we train different deep neural network classification models on a publicly available annotated dataset and study their performances on identifying mentions of personal intake of medicine in tweets. We also design and train a new architecture of a stacked ensemble of shallow convolutional neural network (CNN) ensembles. We use random search for tuning the hyperparameters of the models and share the details of the values taken by the hyperparameters for the best learnt model in different deep neural network architectures. Our system produces state-of-the-art results, with a micro- averaged F-score of 0.693.