Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time.
Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time.
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DOI：
10.18653/v1/2020.acl-demos.9
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Wallace BC
中科院分区：
文献类型：
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作者：
Nye BE;Nenkova A;Marshall IJ;Wallace BC
我们介绍Trialstreamer，一个活生生的临床试验报告数据库。这里我们主要描述了证据提取部分；它从生物医学摘要中提取了临床医生在评价文献时需要的关键信息，以及这些信息之间的关系。具体来说，该系统提取了试验参与者的描述，每个组中比较的治疗方法（干预措施），以及测量了哪些结果。然后，该系统试图通过确定干预措施与确定的试验结果措施的关系来推断哪些干预措施被报告为效果最好。除了总结单个试验之外，这些提取的数据元素还允许对同一主题的许多试验的结果进行自动合成。我们将该系统大规模应用于MEDLINE索引的所有随机对照试验报告，为证据图的自动生成提供动力，这些证据图结合了来自所有相关临床试验的数据，提供了不同干预措施有效性的全局视图。我们将所有代码和模型免费提供给用户1，并提供web界面演示
We introduce Trialstreamer, a living database of clinical trial reports. Here we mainly describe the evidence extraction component; this extracts from biomedical abstracts key pieces of information that clinicians need when appraising the literature, and also the relations between these. Specifically, the system extracts descriptions of trial participants, the treatments compared in each arm (the interventions), and which outcomes were measured. The system then attempts to infer which interventions were reported to work best by determining their relationship with identified trial outcome measures. In addition to summarizing individual trials, these extracted data elements allow automatic synthesis of results across many trials on the same topic. We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of evidence maps, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic. We make all code and models freely available1 alongside a demonstration of the web interface.2