连接罕见和常见的疾病词汇之间的映射人类表型本体和密码。

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES
Evonne McArthur, Lisa Bastarache, John A Capra
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引用次数: 2

摘要

要想发现罕见病和常见病,就需要将生物学知识与临床数据相结合;然而,术语上的差异是一个主要障碍。例如,人类表型本体(HPO)是描述罕见疾病特征的主要词汇,而大多数临床遇到使用国际疾病分类(ICD)计费代码。ICD代码通过显码进一步组织成临床有意义的表型。尽管HPO和phecodes/ICD普遍存在,但在HPO和phecodes/ICD之间没有强有力的全现象疾病图谱。在这里,我们使用不同的来源和方法(包括文本匹配、国家医学图书馆的统一医学语言系统(UMLS)、Wikipedia、SORTA和phemap)综合证据,通过38950个链接定义代码和HPO术语之间的映射。我们评估了每个证据领域的精确度和召回率,无论是单独的还是联合的。这种灵活性允许用户为单基因到多基因疾病的各种应用量身定制HPO-phecode链接。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Linking rare and common disease vocabularies by mapping between the human phenotype ontology and phecodes.

Linking rare and common disease vocabularies by mapping between the human phenotype ontology and phecodes.

Linking rare and common disease vocabularies by mapping between the human phenotype ontology and phecodes.

Linking rare and common disease vocabularies by mapping between the human phenotype ontology and phecodes.

Enabling discovery across the spectrum of rare and common diseases requires the integration of biological knowledge with clinical data; however, differences in terminologies present a major barrier. For example, the Human Phenotype Ontology (HPO) is the primary vocabulary for describing features of rare diseases, while most clinical encounters use International Classification of Diseases (ICD) billing codes. ICD codes are further organized into clinically meaningful phenotypes via phecodes. Despite their prevalence, no robust phenome-wide disease mapping between HPO and phecodes/ICD exists. Here, we synthesize evidence using diverse sources and methods-including text matching, the National Library of Medicine's Unified Medical Language System (UMLS), Wikipedia, SORTA, and PheMap-to define a mapping between phecodes and HPO terms via 38 950 links. We evaluate the precision and recall for each domain of evidence, both individually and jointly. This flexibility permits users to tailor the HPO-phecode links for diverse applications along the spectrum of monogenic to polygenic diseases.

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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
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