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Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System

机译:在Twitter中检测个人药物摄入量:带注释的语料库和基线分类系统

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摘要

Social media sites (e.g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data. Mining social media data for this information would require the ability to distinguish indications of personal medication intake in this media. Towards that end, this paper presents an annotated corpus that can be used to train machine learning systems to determine whether a tweet that mentions a medication indicates that the individual posting has taken that medication (at a specific time). To demonstrate the utility of the corpus as a training set, we present baseline results of supervised classification.
机译:社交媒体网站(例如Twitter)已用于监视人群一级的药物安全性,但是专注于药物对特定人群的影响的研究不得不依靠其他数据来源。为此信息挖掘社交媒体数据将需要能够区分该媒体中个人药物摄入量的指示。为此,本文提出了一种带注释的语料库,该语料库可用于训练机器学习系统,以确定提及药物的推文是否表明个人张贴了该药物(在特定时间)。为了证明语料库作为训练集的效用,我们介绍了监督分类的基线结果。

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  • 会议地点 Vancouver(CA)
  • 作者单位

    Department of Biostatistics, Epidemiology, and Informatics Perelman School of Medicine University of Pennsylvania;

    Department of Biostatistics, Epidemiology, and Informatics Perelman School of Medicine University of Pennsylvania;

    Department of Biostatistics, Epidemiology, and Informatics Perelman School of Medicine University of Pennsylvania;

    Department of Biostatistics, Epidemiology, and Informatics Perelman School of Medicine University of Pennsylvania;

    Department of Biostatistics, Epidemiology, and Informatics Perelman School of Medicine University of Pennsylvania;

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