Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.
Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.
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DOI：
10.1371/journal.pone.0162287
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Ananiadou S
中科院分区：
文献类型：
--
作者：
Alnazzawi N;Thompson P;Ananiadou S
来自电子健康记录（EHR）的生物医学文献文章和叙述性内容都构成了疾病表型信息的丰富来源。表型概念可以在文本中以多种方式提及，使用具有各种结构的短语。这种差异性部分源于作者的不同背景，但也源于每种文本类型通常使用的不同写作风格。由于EHR叙述性报告和文献文章包含不同但互补的有价值信息类型，因此将每种文本类型的详细信息结合起来有助于发现新的疾病-表型关联。然而，在每一来源中提及同一概念的其他方式构成了自动综合信息的障碍。因此，识别文本中由短语表示的独特概念可以帮助弥合文本类型之间的差距。我们描述了我们开发的一种新的方法，PhenoNorm，它集成了一些不同的相似性措施，允许自动链接的表型概念提到的UMLS元词库，生物医学术语资源中的已知概念。PhenoNorm是使用PhenoCHF语料库开发的，该语料库是EHR中的文献文章和叙述的集合，注释了与充血性心力衰竭（CHF）相关的表型信息。我们评估了PhenoNorm在将CHF相关表型提及与元词库概念联系起来方面的性能，使用新丰富的PhenoCHF版本，其中每个表型提及都与UMLS元词库中的概念有专家验证的链接。我们表明，PhenoNorm优于一些替代方法适用于相同的任务。此外，我们证明了PhenoNorm的更广泛的实用性，通过评估其能力链接提到的各种其他类型的医学相关的信息，发生在文本涵盖更广泛的主题领域，在不同的术语资源的概念。我们表明，PhenoNorm可以保持性能水平，并且其准确性与应用于这些任务的其他方法相比毫不逊色。
Biomedical literature articles and narrative content from Electronic Health Records (EHRs) both constitute rich sources of disease-phenotype information. Phenotype concepts may be mentioned in text in multiple ways, using phrases with a variety of structures. This variability stems partly from the different backgrounds of the authors, but also from the different writing styles typically used in each text type. Since EHR narrative reports and literature articles contain different but complementary types of valuable information, combining details from each text type can help to uncover new disease-phenotype associations. However, the alternative ways in which the same concept may be mentioned in each source constitutes a barrier to the automatic integration of information. Accordingly, identification of the unique concepts represented by phrases in text can help to bridge the gap between text types. We describe our development of a novel method, PhenoNorm, which integrates a number of different similarity measures to allow automatic linking of phenotype concept mentions to known concepts in the UMLS Metathesaurus, a biomedical terminological resource. PhenoNorm was developed using the PhenoCHF corpus—a collection of literature articles and narratives in EHRs, annotated for phenotypic information relating to congestive heart failure (CHF). We evaluate the performance of PhenoNorm in linking CHF-related phenotype mentions to Metathesaurus concepts, using a newly enriched version of PhenoCHF, in which each phenotype mention has an expert-verified link to a concept in the UMLS Metathesaurus. We show that PhenoNorm outperforms a number of alternative methods applied to the same task. Furthermore, we demonstrate PhenoNorm’s wider utility, by evaluating its ability to link mentions of various other types of medically-related information, occurring in texts covering wider subject areas, to concepts in different terminological resources. We show that PhenoNorm can maintain performance levels, and that its accuracy compares favourably to other methods applied to these tasks.
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