Automated chronic pain cohort mined from Twitter using machine learning – Medical Xpress

End-to-end data processing framework involving data collection/storage via the Twitter API, data preparation and preprocessing, training, evaluation, and classification using the final model and postclassification analyses. Credit: Health Data Science (2023). DOI: 10.34133/hds.0078 Social media platforms like Twitter (now X) offer valuable insights into self-reported chronic pain, according to a recent study by multiple institutions. The researchers have automated the process of establishing a chronic pain cohort, setting the stage for future data mining and causal association studies. The study was published in Health Data Science. “We aimed to detect large-scale discussions related to chronic pain on Twitter, develop methods for automatic detection of self-disclosures, and collect and analyze longitudinal data,” said Abeed Sarker, associate professor at Emory University. Chronic pain and subsequent opioid use pose significant health and economic challenges. Gathering public knowledge and experiences could guide alternative therapies for specific pain types. The team collected data via Twitter’s academic API, followed by manual annotation and classification. For instance, posts mentioned therapies like meditation and chiropractic, and sentiment analysis revealed varying public opinions on these therapies. “Social media is a rich source for chronic pain-related information. Methods like NLP and machine learning can extract key insights from this large
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