使用机器学习和深度学习方法在社交媒体中查找药物不良反应的提及

Pilar López Úbeda, Manuel Carlos Díaz Galiano, Maite Martin, L. Ureña López
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引用次数: 6

摘要

随着时间的推移,社交网络的使用正在成为分享健康相关信息的非常流行的平台。针对健康应用程序的社交媒体挖掘(SMM4H)提供了诸如本文中描述的任务,以帮助管理健康领域中的信息。这份文件显示了西奈小组的首次参与。我们研究了基于机器学习和深度学习的方法来从Twitter中提取药物不良反应。在任务中获得的结果是令人鼓舞的,我们接近所有参与者的平均水平,在某些情况下甚至高于。
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Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media
Over time the use of social networks is becoming very popular platforms for sharing health related information. Social Media Mining for Health Applications (SMM4H) provides tasks such as those described in this document to help manage information in the health domain. This document shows the first participation of the SINAI group. We study approaches based on machine learning and deep learning to extract adverse drug reaction mentions from Twitter. The results obtained in the tasks are encouraging, we are close to the average of all participants and even above in some cases.
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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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