Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization.

IF 1.3 4区医学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

Methods of Information in Medicine Pub Date : 2020-08-01 Epub Date: 2020-11-04 DOI:10.1055/s-0040-1718940

Qian Zhu, Dac-Trung Nguyen, Eric Sid, Anne Pariser

{"title":"Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization.","authors":"Qian Zhu, Dac-Trung Nguyen, Eric Sid, Anne Pariser","doi":"10.1055/s-0040-1718940","DOIUrl":null,"url":null,"abstract":"Objective: In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease.Methods: We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study.Results: We found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%).Conclusion: The mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"59 4-05","pages":"131-139"},"PeriodicalIF":1.3000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1055/s-0040-1718940","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Methods of Information in Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1055/s-0040-1718940","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/11/4 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 2

Abstract

Objective: In this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease.

Methods: We analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study.

Results: We found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%).

Conclusion: The mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.

查看原文本刊更多论文

利用UMLS作为罕见病数据规范化和协调的数据标准。

目的:在本研究中，我们旨在评估统一医学语言系统(UMLS)作为一种数据标准的能力，以支持罕见病数据集的数据规范化和协调。通过分析多个罕见病资源与UMLS之间的数据映射，我们提出了UMLS的扩展建议，使其能够作为罕见病的全球标准。方法:我们分析了UMLS和现有数据集之间的数据映射，这些数据集来自四个可公开访问的资源:遗传和罕见病信息中心(GARD)、Orphanet、男性在线孟德尔遗传(OMIM)和君主病本体(MONDO)。评估了两种类型的疾病映射，(1)从这四种资源中提取的策划映射;(2)通过查询前人开发的基于罕见病的整合知识图谱建立映射。结果:我们发现100%的OMIM概念和超过50%的GARD、MONDO和Orphanet概念被UMLS标准化，并准确地分类到适当的UMLS语义组中。我们分析了58,636个UMLS映射，在这些资源中产生了3,876个UMLS概念。对500个随机的UMLS映射进行人工评估，结果显示这些映射的开发准确率很高(99%)，其中包括414个同义词映射(82.8%)，76个亚型映射(15.2%)和5个兄弟姐妹映射(1%)。结论:本研究的制图结果表明，UMLS能够准确地表示罕见病概念及其相关信息，如基因和表型，并且可以有效地用于支持收集罕见病数据的现有资源之间的数据协调。我们建议采用UMLS作为罕见病的数据标准，使现有的罕见病数据集能够支持未来在临床和社区环境中的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Methods of Information in Medicine 医学-计算机：信息系统

CiteScore

3.70

自引率

11.80%

发文量

审稿时长

6-12 weeks

期刊介绍： Good medicine and good healthcare demand good information. Since the journal''s founding in 1962, Methods of Information in Medicine has stressed the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care. Covering publications in the fields of biomedical and health informatics, medical biometry, and epidemiology, the journal publishes original papers, reviews, reports, opinion papers, editorials, and letters to the editor. From time to time, the journal publishes articles on particular focus themes as part of a journal''s issue.