about
Utilizing social media data for pharmacovigilance: A reviewPharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster featuresPortable automatic text classification for adverse drug reaction detection via multi-corpus training.Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter.Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum postsA corpus for mining drug-related knowledge from Twitter chatter: Language models and their utilities.Query-oriented evidence extraction to support evidence-based medicine practice.Automatic evidence quality prediction to support evidence-based decision making.Extractive summarisation of medical documents using domain knowledge and corpus statistics.Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis.Authors' Reply to Jouanjus and Colleagues' Comment on "Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter".Dermatologic concerns communicated through Twitter.Deep neural networks and distant supervision for geographic location mention extraction.Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared taskSocial Media Mining for Birth Defects Research: A Rule-Based, Bootstrapping Approach to Collecting Data for Rare Health-Related Events on TwitterComment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts"Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and GuidelinesDeep neural networks ensemble for detecting medication mentions in tweetsMethods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with AdalimumabAn interpretable natural language processing system for written medical examination assessmentTowards Automating Location-Specific Opioid Toxicosurveillance from Twitter via Data Science MethodsAn unsupervised and customizable misspelling generator for mining noisy health-related text sourcesSelf-reported COVID-19 symptoms on Twitter: An analysis and a research resource
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description
investigador
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Abeed Sarker
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Abeed Sarker
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Abeed Sarker
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0000-0001-7358-544X