ArticleNPJ digital medicine2019
Towards scaling Twitter for digital epidemiology of birth defects.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Using Twitter Data for Cohort Studies of Drug Safety in Pregnancy: Proof-of-concept With β-Blockers.JMIR formative research · 2022Article
- Toward Using Twitter Data to Monitor COVID-19 Vaccine Safety in Pregnancy: Proof-of-Concept Study of Cohort Identification.JMIR formative research · 2022Article
- Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing.Journal of pathology informatics · 2022Article
- Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes.Journal of medical Internet research · 2021Article
- A New Era in Pharmacovigilance: Toward Real-World Data and Digital Monitoring.Clinical pharmacology and therapeutics · 2021Review
- A natural language processing pipeline to advance the use of Twitter data for digital epidemiology of adverse pregnancy outcomes.Journal of biomedical informatics · 2020Article
- Pregnancy and health in the age of the Internet: A content analysis of online "birth club" forums.PloS one · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Social media has recently been used to identify and study a small cohort of Twitter users whose pregnancies with birth defect outcomes-the leading cause of infant mortality-could be observed via their publicly available tweets. In this study, we exploit social media on a larger scale by developing natural language processing (NLP) methods to automatically detect, among thousands of users, a cohort of mothers reporting that their child has a birth defect. We used 22,999 annotated tweets to train and evaluate supervised machine learning algorithms-feature-engineered and deep learning-based classifiers-that automatically distinguish tweets referring to the user's pregnancy outcome from tweets that merely mention birth defects. Because 90% of the tweets merely mention birth defects, we experimented with under-sampling and over-sampling approaches to address this class imbalance. An SVM classifier achieved the best performance for the two positive classes: an F
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.