Methods for leveraging social media data to quantify and improve pharmacovigilance efforts
Abstract/Contents
- Abstract
- Adverse drug reactions impact the health of 100,000s of individuals annually in the United States with associated costs in the hundreds of billions. Pharmacovigilance seeks to improve drug safety and limit patient potential for adverse drug events. Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data as a component of pharmacovigilance efforts has been hindered by the massive and noisy nature of the data. I present work seeking to (1) identify social media data discussions of drug and drug-related terms, (2) use word embeddings derived from social media data to create quantitative severity scores for adverse drug reactions, and (3) create digital cohorts of social media users and monitor them for changes in prevalence of drug discussion rates in the general US potential, specifically with regards opioids. I present novel methods that enabled these efforts, as well as the results and findings of these efforts. I believe leveraging social media data to enhance pharmacovigilance will benefit U.S. patient populations and further empower public health and regulatory efforts
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Lavertu, Adam Allen Joseph |
|---|---|
| Degree supervisor | Altman, Russ |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Chen, Jonathan H |
| Thesis advisor | Eichstaedt, Johannes C |
| Thesis advisor | Liang, Percy |
| Degree committee member | Chen, Jonathan H |
| Degree committee member | Eichstaedt, Johannes C |
| Degree committee member | Liang, Percy |
| Associated with | Stanford University, School of Medicine, Department of Biomedical Data Science |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Adam Lavertu |
|---|---|
| Note | Submitted to the Department of Biomedical Data Science |
| Thesis | Thesis Ph.D. Stanford University 2021 |
| Location | https://purl.stanford.edu/zv853wv3862 |
Access conditions
- Copyright
- © 2021 by Adam Allen Joseph Lavertu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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