دورية أكاديمية
#ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning
العنوان: | #ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning |
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المؤلفون: | Abeed Sarker, Sahithi Lakamana, Yuting Guo, Yao Ge, Abimbola Leslie, Omolola Okunromade, Elena Gonzalez-Polledo, Jeanmarie Perrone, Anne Marie McKenzie-Brown |
المصدر: | Health Data Science, Vol 3 (2023) |
بيانات النشر: | American Association for the Advancement of Science (AAAS), 2023. |
سنة النشر: | 2023 |
المجموعة: | LCC:Computer applications to medicine. Medical informatics |
مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7 |
الوصف: | Background: Due to the high burden of chronic pain, and the detrimental public health consequences of its treatment with opioids, there is a high-priority need to identify effective alternative therapies. Social media is a potentially valuable resource for knowledge about self-reported therapies by chronic pain sufferers. Methods: We attempted to (a) verify the presence of large-scale chronic pain-related chatter on Twitter, (b) develop natural language processing and machine learning methods for automatically detecting self-disclosures, (c) collect longitudinal data posted by them, and (d) semiautomatically analyze the types of chronic pain-related information reported by them. We collected data using chronic pain-related hashtags and keywords and manually annotated 4,998 posts to indicate if they were self-reports of chronic pain experiences. We trained and evaluated several state-of-the-art supervised text classification models and deployed the best-performing classifier. We collected all publicly available posts from detected cohort members and conducted manual and natural language processing-driven descriptive analyses. Results: Interannotator agreement for the binary annotation was 0.82 (Cohen’s kappa). The RoBERTa model performed best (F1 score: 0.84; 95% confidence interval: 0.80 to 0.89), and we used this model to classify all collected unlabeled posts. We discovered 22,795 self-reported chronic pain sufferers and collected over 3 million of their past posts. Further analyses revealed information about, but not limited to, alternative treatments, patient sentiments about treatments, side effects, and self-management strategies. Conclusion: Our social media based approach will result in an automatically growing large cohort over time, and the data can be leveraged to identify effective opioid-alternative therapies for diverse chronic pain types. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 2765-8783 33982368 |
Relation: | https://doaj.org/toc/2765-8783 |
DOI: | 10.34133/hds.0078 |
URL الوصول: | https://doaj.org/article/dedf9975fbeb4e0da33982368fc588fe |
رقم الأكسشن: | edsdoj.f9975fbeb4e0da33982368fc588fe |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 27658783 33982368 |
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DOI: | 10.34133/hds.0078 |