识别推文中提及的药物不良反应- SMM4H共享任务2019

Samarth Rawal, S. Rawal, Saadat Anwar, Chitta Baral
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引用次数: 1

摘要

分析社交媒体帖子可以深入了解网上经常讨论的广泛主题,为研究网上报道的各种与健康相关的现象提供有价值的信息。这项工作的结果可以为药物警戒研究提供见解,以监测药物的不良反应。本研究通过2019年社交媒体健康应用挖掘(SMM4H)共享任务专门研究了Twitter数据中提及的药物不良反应(adr)。药物不良反应是由药物或其他治疗方法引起的不希望的有害影响。本研究的目标是使用自然语言处理技术建立准确的模型,以检测Twitter数据中的药物不良反应报告,并提取这些单词或短语。
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Identification of Adverse Drug Reaction Mentions in Tweets – SMM4H Shared Task 2019
Analyzing social media posts can offer insights into a wide range of topics that are commonly discussed online, providing valuable information for studying various health-related phenomena reported online. The outcome of this work can offer insights into pharmacovigilance research to monitor the adverse effects of medications. This research specifically looks into mentions of adverse drug reactions (ADRs) in Twitter data through the Social Media Mining for Health Applications (SMM4H) Shared Task 2019. Adverse drug reactions are undesired harmful effects which can arise from medication or other methods of treatment. The goal of this research is to build accurate models using natural language processing techniques to detect reports of adverse drug reactions in Twitter data and extract these words or phrases.
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Approaching SMM4H with Merged Models and Multi-task Learning BIGODM System in the Social Media Mining for Health Applications Shared Task 2019 HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets Lexical Normalization of User-Generated Medical Text Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1
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