Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.
Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.
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DOI：
10.1186/s13326-015-0004-6
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发表时间：
2015
影响因子：
1.9
通讯作者：
Ananiadou S
中科院分区：
文献类型：
--
作者：
Fu X;Batista-Navarro R;Rak R;Ananiadou S
慢性阻塞性肺疾病（COPD）是一种危及生命的肺部疾病，其最近的流行导致公共医疗保健负担日益增加。电子临床记录中的表型信息对于为COPD患者提供合适的个性化治疗至关重要。然而，由于表型通常“隐藏”在临床记录中的自由文本中，临床医生可以从促进其迅速识别的文本挖掘系统中受益。本文报道了一种半自动的方法，用于产生一个语料库，最终可以支持开发的文本挖掘工具，反过来，将加快识别COPD患者群体的过程。根据COPD专家的专业知识选择标准，形成了30篇全文论文的语料库。我们开发了一种注释方案，旨在生成细粒度、富有表现力且可计算的COPD注释，而不会给我们的策展人带来高度复杂的任务负担。这是在Argo平台中通过半自动注释工作流程实现的，该工作流程集成了几个文本挖掘工具，包括用于标记文档的图形用户界面。当使用金标准（即，手动验证）注释，半自动工作流程被示出获得45.70%的微平均F分数（具有松弛匹配）。利用金标准数据来训练新概念识别器，我们证明了我们的语料库虽然仍在进行中，但可以促进开发性能更好的COPD表型提取器。我们在这项工作中描述了我们旨在最终支持COPD表型治疗过程的方法，即，通过应用集成到注释工作流中的各种文本挖掘工具。虽然所描述的语料库仍在开发中，我们的结果迄今为止是令人鼓舞的，并显示出巨大的潜力，刺激进一步的自动COPD表型提取器的发展。本文的在线版本（doi：10.1186/s13326-015-0004-6）包含补充材料，可供授权用户使用。
Chronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. However, as phenotypes are often “hidden” within free text in clinical records, clinicians could benefit from text mining systems that facilitate their prompt recognition. This paper reports on a semi-automatic methodology for producing a corpus that can ultimately support the development of text mining tools that, in turn, will expedite the process of identifying groups of COPD patients. A corpus of 30 full-text papers was formed based on selection criteria informed by the expertise of COPD specialists. We developed an annotation scheme that is aimed at producing fine-grained, expressive and computable COPD annotations without burdening our curators with a highly complicated task. This was implemented in the Argo platform by means of a semi-automatic annotation workflow that integrates several text mining tools, including a graphical user interface for marking up documents. When evaluated using gold standard (i.e., manually validated) annotations, the semi-automatic workflow was shown to obtain a micro-averaged F-score of 45.70% (with relaxed matching). Utilising the gold standard data to train new concept recognisers, we demonstrated that our corpus, although still a work in progress, can foster the development of significantly better performing COPD phenotype extractors. We describe in this work the means by which we aim to eventually support the process of COPD phenotype curation, i.e., by the application of various text mining tools integrated into an annotation workflow. Although the corpus being described is still under development, our results thus far are encouraging and show great potential in stimulating the development of further automatic COPD phenotype extractors. The online version of this article (doi:10.1186/s13326-015-0004-6) contains supplementary material, which is available to authorized users.
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影响因子：
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通讯作者：
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通讯作者：
Mabee PM