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首页> 外文会议>International Arab Conference on Information Technology >Mining Patients' Reviews in Online Health Communities for Adverse Drug Reaction Detection of Antiepileptic Drugs
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Mining Patients' Reviews in Online Health Communities for Adverse Drug Reaction Detection of Antiepileptic Drugs

机译：在线卫生社区中的矿产患者评论因抗癫痫药物的不良药物反应检测

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摘要

In pharmacovigilance, the detection of adverse drug reactions is a task of utmost importance. This paper presents a data mining-based method to detect adverse drug reactions of anti-epileptic drugs from a dataset of patients' reviews collected from an online health community. The dataset is preprocessed and the unigram, bigram, and trigram are generated and then the adverse drug reactions of each anti-epileptic drug are extracted with the help of consumer health vocabulary and adverse drug reactions lexicon. Proportional reporting ratio is used to measure the association between each adverse drug reaction and antiepileptic drug. A list of ranked adverse drug reactions for each anti-epileptic drug is generated and validated against Drugs.com database. The results show the validity and utility of using patients' reviews in online health communities as a source for adverse drug reactions detection.
机译：在药物检测中，不良药物反应的检测是最重要的任务。本文介绍了一种基于数据的方法，以检测来自在线健康界收集的患者评论的数据集的抗癫痫药物的不良药物。该数据集是预处理的，并且产生了Unigram，Bigram和Trigram，然后在消费者健康词汇和不良药物exexicon的帮助下提取每种抗癫痫药物的不良药物。比例报告率用于测量每个不良药物反应和抗癫痫药物之间的关联。生成和验证对药物的排名不良药物反应列表。结果表明，在线健康社区中使用患者评论的有效性和效用作为不利药物反应检测的来源。

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