Marshall Iain J, Kuiper Joël, Banner Edward, Wallace Byron C
Department of Primary Care and Public Health Sciences, Kings College London.
Doctor Evidence.
Proc Conf Assoc Comput Linguist Meet. 2017 Jul;2017:7-12. doi: 10.18653/v1/P17-4002.
We present , an open-source web-based system that uses machine learning and NLP to semi-automate biomedical evidence synthesis, to aid the practice of Evidence-Based Medicine. RobotReviewer processes full-text journal articles (PDFs) describing randomized controlled trials (RCTs). It appraises the reliability of RCTs and extracts text describing key trial characteristics (e.g., descriptions of the population) using novel NLP methods. RobotReviewer then automatically generates a report synthesising this information. Our goal is for RobotReviewer to automatically extract and synthesise the full-range of structured data needed to inform evidence-based practice.
我们展示了一个基于网络的开源系统，该系统利用机器学习和自然语言处理技术来半自动化生物医学证据合成，以辅助循证医学实践。RobotReviewer处理描述随机对照试验（RCT）的全文期刊文章（PDF格式）。它评估随机对照试验的可靠性，并使用新颖的自然语言处理方法提取描述关键试验特征（如人群描述）的文本。然后，RobotReviewer会自动生成一份综合这些信息的报告。我们的目标是让RobotReviewer自动提取和合成循证实践所需的全方位结构化数据。
