Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media
Name
2102.06836.pdf
Description
Accepted version
Size
2.37 MB
Format
Adobe PDF
Checksum (MD5)
228f390f1cef8bee460fa0bd9fad4708
Author(s)
Wu, Julia
Sivaraman, Venkatesh
Kumar, Dheekshita
Banda, Juan M.
Sontag, David Alexander
Date Issued
August 2021
Journal
Journal of Biomedical Informatics
Publisher
Elsevier BV
Citation
Wu, Julia et al. "Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media." Journal of Biomedical Informatics 120 (August 2021): 103844. © 2021 Elsevier
Version
Author's final manuscript
Abstract
The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals
(HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labelingis a challenge because of the sheer volume of irrelevant data. We present an
unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method
can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.jbi.2021.103844
