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KNOWLEDGE DISCOVERY FROM SOCIAL MEDIA AND BIOMEDICAL LITERATURE FOR ADVERSE DRUG EVENTS

机译:社交媒体和生物医学文献对不良药物事件的知识发现

摘要

In adverse drug event (ADE) monitoring and reporting, drug-related messages (60) are detected in one or more social media message streams as messages that include a name of a monitored drug. ADE reports (62) are extracted from the drug-related messages using an ADE classifier (46). The extracted ADE reports are validated by comparison with known ADEs of the monitored drug stored in an ADE knowledge base (64). Extracted ADE reports that fail the validating are collected in a non-validated ADE reports database (72). A report (74) is generated including information on at least one previously unrecognized ADE for which extracted ADE reports in the non-validated ADE reports database satisfy a previously unrecognized ADE criterion (in terms of number of messages or number of unique patients reporting the ADE).
机译:在不良药品事件(ADE)监视和报告中,与毒品有关的消息(60)在一个或多个社交媒体消息流中被检测为包含受监视毒品名称的消息。使用ADE分类器(46)从毒品相关消息中提取ADE报告(62)。通过与存储在ADE知识库中的受监控药物的已知ADE进行比较来验证提取的ADE报告(64)。未能通过验证的提取的ADE报告被收集在未验证的ADE报告数据库中(72)。生成报告(74),该报告包括关于至少一个先前无法识别的ADE的信息,对于该信息,未验证的ADE报告数据库中提取的ADE报告满足先前无法识别的ADE标准(就消息的数量或报告ADE的唯一患者数而言) )。

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