Drugs undergo extensive testing in animals and clinical trials in humans before they are marketed for widespread use. Pre-market testing produces reasonably high quality information about the efficacy of the drug as a treatment for the condition for which it was approved, but gives a very incomplete picture of the drug's safety. It is only after a drug is marketed and used on a more widespread basis over longer periods of time that it is possible to identify other effects, such as rare but serious adverse effects, or those that are more common in the special subgroups excluded from the trial (such as pregnant women), or effects of long-term use of the drug, among others. Despite the increase in research in the past years exploring social media data for pharmacovigilance, and the evidence that it indeed can bring forward the patient perspective, there is no systematic approach to collect and annotate such data for research purposes. This renewal builds on our prior research and natural language processing (NLP) methods for social media mining in pharmacovigilance to make the collection of social media data about medication use precise and systematic enough to be useful to researchers and the public, alongside established sources such as the FDA's data and other public collections of drug adverse event data. It presents innovative methods to automatically collect and analyze longitudinal health data, piloting methods for interventions through the same media that can inform the public and help validate the automatic methods. As validation, we include a comparison to an existing reference standard for adverse effects that integrates FDA's data and HER data, as well as specific case studies focused on (Aim 3.1) the use of NSAIDs and anti-depressants in pregnancy and (Aim 3.2) factors for non-adherence.

Public Health Relevance

Pre-market testing of medications produces reasonably high quality information about the efficacy of the drug as a treatment for the condition for which it was approved, but gives a very incomplete picture of the drug's safety. It is only after a drug is marketed and used on a more widespread basis over longer periods of time that it is possible to identify other effects, such as rare but serious adverse effects, or those that are more common in the special subgroups excluded from the trial (such as pregnant women), or effects of long-term use of the drug, among others. This renewal application builds on our prior research and natural language processing (NLP), developing novel methods for data extraction that makes it possible to integrate information from social media with existing drug safety information and extract health data over time.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
2R01LM011176-06A1
Application #
9598270
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Vanbiervliet, Alan
Project Start
2012-09-10
Project End
2022-05-31
Budget Start
2018-09-13
Budget End
2019-05-31
Support Year
6
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Smith, Karen; Golder, Su; Sarker, Abeed et al. (2018) Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab. Drug Saf 41:1397-1410
Sarker, Abeed; Gonzalez-Hernandez, Graciela (2018) An unsupervised and customizable misspelling generator for mining noisy health-related text sources. J Biomed Inform 88:98-107
Klein, Ari Z; Sarker, Abeed; Cai, Haitao et al. (2018) Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter. J Biomed Inform 87:68-78
Sarker, Abeed; Nikfarjam, Azadeh; Gonzalez, Graciela (2016) SOCIAL MEDIA MINING SHARED TASK WORKSHOP. Pac Symp Biocomput 21:581-92
Sarker, Abeed; O'Connor, Karen; Ginn, Rachel et al. (2016) Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter. Drug Saf 39:231-40
Sullivan, Ryan; Sarker, Abeed; O'Connor, Karen et al. (2016) FINDING POTENTIALLY UNSAFE NUTRITIONAL SUPPLEMENTS FROM USER REVIEWS WITH TOPIC MODELING. Pac Symp Biocomput 21:528-39
Korkontzelos, Ioannis; Nikfarjam, Azadeh; Shardlow, Matthew et al. (2016) Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts. J Biomed Inform 62:148-58
Gonzalez, Graciela H; Tahsin, Tasnia; Goodale, Britton C et al. (2016) Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery. Brief Bioinform 17:33-42
Paul, Michael J; Sarker, Abeed; Brownstein, John S et al. (2016) SOCIAL MEDIA MINING FOR PUBLIC HEALTH MONITORING AND SURVEILLANCE. Pac Symp Biocomput 21:468-79
Sarker, Abeed; Gonzalez, Graciela (2015) Portable automatic text classification for adverse drug reaction detection via multi-corpus training. J Biomed Inform 53:196-207

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