Abstract

Objective

Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in health care but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports.

Materials and Methods

Trialstreamer continuously monitors PubMed and the World Health Organization International Clinical Trials Registry Platform, looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial PICO (populations, interventions/comparators, and outcomes) elements and map these snippets to normalized MeSH (Medical Subject Headings) vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database, which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies.

Results

As of early June 2020, we have indexed 673 191 publications of RCTs, of which 22 363 were published in the first 5 months of 2020 (142 per day). We additionally include 304 111 trial registrations from the International Clinical Trials Registry Platform. The median trial sample size was 66.

Conclusions

We present an automated system for finding and categorizing RCTs. This yields a novel resource: a database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (https://trialstreamer.robotreviewer.net).

中文翻译：

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Trialstreamer：一个实时的，自动更新的临床试验报告数据库。

摘要

目的

随机对照试验（RCT）是评估某种疗法是否在医疗保健中有效但很难找到和利用的金标准方法。我们描述了系统的开发和评估，以自动查找和分类所有新的RCT报告。

材料和方法

Trialstreamer持续监控PubMed和世界卫生组织国际临床试验注册平台，使用经过验证的分类器寻找人类的新RCT。我们将机器学习和基于规则的方法相结合，以从RCT摘要中提取信息，包括试验性PICO（人群，干预措施/比较者和结果）元素的自由文本描述，并将这些摘要映射到标准化的MeSH（医学主题词）词汇。我们还确定样本量，预测存在偏见的风险，并提取传达关键发现的文字。我们将所有提取的数据存储在数据库中，并通过搜索门户免费提供下载，并允许用户输入结构化的临床查询。结果会自动排名，以优先处理较大和较高质量的研究。

结果

截至2020年6月上旬，我们已为673191篇RCT出版物编制了索引，其中2020年头5个月出版了22363篇（每天142篇）。此外，我们还包括来自国际临床试验注册平台的304111次试验注册。试验样本中位数为66。

结论

我们提供了一个用于查找和分类RCT的自动化系统。这产生了一种新颖的资源：为人类中所有已发布的RCT自动提取的结构化信息数据库。我们会在我们的网站（https://trialstreamer.robotreviewer.net）上提供此数据库的每日更新。