Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision
Extracting PICO Sentences from Clinical Trial Reports using Supervised Distant Supervision
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发表时间：
2016
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通讯作者：
Byron C. Wallace;J. Kuiper;Aakash Sharma;Mingxi Zhu;I. Marshall
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作者：
Byron C. Wallace;J. Kuiper;Aakash Sharma;Mingxi Zhu;I. Marshall
系统性综述通过全面综合所有相关的已发表证据来解决精确的临床问题，从而支持循证医学（EBM）。系统性综述的作者通常定义关注的人群/问题、干预、对照和结局（皮科标准），然后检索、评价和综合符合这些标准的所有临床试验报告的结果。因此，在试验报告全文中识别皮科元素是系统性综述过程中关键但耗时的一步。我们寻求通过开发机器学习模型来加快证据合成，以自动从与皮科元素相关的文章中提取句子。为这项任务收集大量的训练数据将是非常昂贵的。因此，我们得到了远程监督（DS），使用以前进行的审查来训练模型。DS需要从可用的结构化资源中抽象地导出“软”标签。然而，我们只能访问相应文章的皮科元素的非结构化、自由文本摘要;我们必须从这些内容中获得所需的文档级注释。为此，我们提出了一种新的方法-监督远程监督（SDS）-使用少量的直接监督，以更好地利用大型语料库的远程标记的实例，通过学习伪注释的文章使用可用的DS。我们表明，这种方法往往优于现有的方法自动皮科提取。
Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive distant supervision (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving 'soft' labels from an available structured resource. However, we have access only to unstructured, free-text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence-level annotations. To this end, we propose a novel method - supervised distant supervision (SDS) - that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo-annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction.