Publication Type
Journal Article
Version
acceptedVersion
Publication Date
7-2018
Abstract
Mining social media messages such as tweets, blogs, and Facebook posts 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 to 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 groups of people, identifying posts mentioning intake of medicine by the user is necessary. Toward this objective we develop a classifier for identifying mentions of personal intake of medicine in tweets. We train a stacked ensemble of shallow convolutional neural network (CNN) models on an annotated dataset. We use random search for tuning the hyper-parameters of the CNN models and present an ensemble of best models for the prediction task. Our system produces state-of-the-art results, with a micro-averaged F-score of 0.693. We believe that the developed classifier has direct uses in the areas of psychology, health informatics, pharmacovigilance, and affective computing for tracking moods, emotions, and sentiments of patients expressing intake of medicine in social media.
Keywords
adverse drug reactions, affective computing, health informatics, personal intake of medicine, pharmacovigilance, social media mining
Discipline
Databases and Information Systems | Health Information Technology | Numerical Analysis and Scientific Computing | Social Media
Research Areas
Data Science and Engineering
Publication
IEEE Intelligent Systems
Volume
33
Issue
4
First Page
87
Last Page
95
ISSN
1541-1672
Identifier
10.1109/MIS.2018.043741326
Publisher
IEEE
Embargo Period
2-22-2023
Citation
MAHATA, Debanjan; FRIEDRICHS, Jasper; SHAH, Rajiv Ratn; and JIANG, Jing.
Detecting personal intake of medicine from Twitter. (2018). IEEE Intelligent Systems. 33, (4), 87-95.
Available at: https://ink.library.smu.edu.sg/sis_research/7765
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/MIS.2018.043741326
Included in
Databases and Information Systems Commons, Health Information Technology Commons, Numerical Analysis and Scientific Computing Commons, Social Media Commons