Grinspan Zachary M, Tian Niu, Yozawitz Elissa G, McGoldrick Patricia E, Wolf Steven M, McDonough Tiffani L, Nelson Aaron, Hafeez Baria, Johnson Stephen B, Hesdorffer Dale C
Weill Cornell Medicine New York New York U.S.A.
Centers for Disease Control and Prevention Atlanta Georgia U.S.A.
Epilepsia Open. 2018 Jan 29;3(1):91-97. doi: 10.1002/epi4.12095. eCollection 2018 Mar.
Identifying individuals with rare epilepsy syndromes in electronic data sources is difficult, in part because of missing codes in the International Classification of Diseases (ICD) system. Our objectives were the following: (1) to describe the representation of rare epilepsies in other medical vocabularies, to identify gaps; and (2) to compile synonyms and associated terms for rare epilepsies, to facilitate text and natural language processing tools for cohort identification and population-based surveillance. We describe the representation of 33 epilepsies in 3 vocabularies: Orphanet, SNOMED-CT, and UMLS-Metathesaurus. We compiled terms via 2 surveys, correspondence with parent advocates, and review of web resources and standard vocabularies. UMLS-Metathesaurus had entries for all 33 epilepsies, Orphanet 32, and SNOMED-CT 25. The vocabularies had redundancies and missing phenotypes. Emerging epilepsies (, and -related epilepsies) were underrepresented. Survey and correspondence respondents included 160 providers, 375 caregivers, and 11 advocacy group leaders. Each epilepsy syndrome had a median of 15 (range 6-28) synonyms. Nineteen had associated terms, with a median of 4 (range 1-41). We conclude that medical vocabularies should fill gaps in representation of rare epilepsies to improve their value for epilepsy research. We encourage epilepsy researchers to use this resource to develop tools to identify individuals with rare epilepsies in electronic data sources.
在电子数据源中识别患有罕见癫痫综合征的个体很困难,部分原因是国际疾病分类(ICD)系统中存在编码缺失的情况。我们的目标如下:(1)描述其他医学词汇表中罕见癫痫的呈现情况,找出差距;(2)编纂罕见癫痫的同义词及相关术语,以促进用于队列识别和基于人群监测的文本及自然语言处理工具的发展。我们描述了3种词汇表(《医学罕见病数据库》、医学系统命名法-临床术语[SNOMED-CT]和统一医学语言系统元词表[UMLS-Metathesaurus])中33种癫痫的呈现情况。我们通过两项调查、与患者家长倡导者的通信以及对网络资源和标准词汇表的审查来编纂术语。UMLS-Metathesaurus中有所有33种癫痫的条目,《医学罕见病数据库》中有32种,SNOMED-CT中有25种。这些词汇表存在冗余和表型缺失的情况。新出现的癫痫(……和……相关癫痫)代表性不足。调查和通信的受访者包括160名医疗服务提供者、375名护理人员和11名倡导团体负责人。每种癫痫综合征的同义词中位数为15个(范围为6 - 28个)。19种有相关术语,中位数为4个(范围为1 - 41个)。我们得出结论,医学词汇表应填补罕见癫痫呈现方面的空白,以提高其在癫痫研究中的价值。我们鼓励癫痫研究人员利用这一资源开发工具,以便在电子数据源中识别患有罕见癫痫的个体。