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Active neural networks to detect mentions of changes to medication treatment in social media.

, , , , , , and . J. Am. Medical Informatics Assoc., 28 (12): 2551-2561 (2021)

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Session Introduction., , , , , , and . PSB, page 468-479. (2016)Utilizing social media data for pharmacovigilance: A review., , , , , , , and . J. Biomed. Informatics, (2015)Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task., , , , , , , , , and 6 other author(s). J. Am. Medical Informatics Assoc., 25 (10): 1274-1283 (2018)ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets., , and . CoRR, (2021)Disease associations depend on visit type: results from a visit-wide association study., , , , and . BioData Min., 12 (1): 15:1-15:10 (2019)An unsupervised and customizable misspelling generator for mining noisy health-related text sources., and . J. Biomed. Informatics, (2018)Towards Automatic Bot Detection in Twitter for Health-related Tasks., , , and . CoRR, (2019)Overview of the Seventh Social Media Mining for Health Applications (#SMM4H) Shared Tasks at COLING 2022., , , , , , , , , and 6 other author(s). SMM4H@COLING, page 221-241. Association for Computational Linguistics, (2022)Dealing with Medication Non-Adherence Expressions in Twitter., , , , and . SMM4H@EMNLP, page 32-33. Association for Computational Linguistics, (2018)Detecting goals of care conversations in clinical notes with active learning., , , , , , , , , and 1 other author(s). J. Biomed. Informatics, (2024)