On Mapping Textual Queries to a Common Data Model

Sijia Liu, Yanshan Wang, Na Hong, F. Shen, Stephen T Wu, W. Hersh, Hongfang Liu
{"title":"On Mapping Textual Queries to a Common Data Model","authors":"Sijia Liu, Yanshan Wang, Na Hong, F. Shen, Stephen T Wu, W. Hersh, Hongfang Liu","doi":"10.1109/ICHI.2017.63","DOIUrl":null,"url":null,"abstract":"The widespread adoption of Electronic Health Records (EHRs) has enabled data-driven approaches to clinical care and research. However, the performance and generalizability of those approaches are severely hampered by the lack of syntactic and semantic interoperability of EHR data across institutions. Towards resolving this problem, Common Data Models (CDMs) can be used to standardize the clinical data in clinical data repositories. In this paper, we described our mapping of entity mention types from patient-level information retrieval queries to an empirical subset of Observational Medical Outcomes Partnership (OMOP) CDM data fields. We investigated the empirical data model by annotating multi-institutional clinical data requests in free text and comparing the distributions of data model fields. The similar distribution of the entity mention types from two different sites indicates that the data model is generalizable for multi-institutional cohort identification queries.","PeriodicalId":263611,"journal":{"name":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Healthcare Informatics (ICHI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHI.2017.63","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

The widespread adoption of Electronic Health Records (EHRs) has enabled data-driven approaches to clinical care and research. However, the performance and generalizability of those approaches are severely hampered by the lack of syntactic and semantic interoperability of EHR data across institutions. Towards resolving this problem, Common Data Models (CDMs) can be used to standardize the clinical data in clinical data repositories. In this paper, we described our mapping of entity mention types from patient-level information retrieval queries to an empirical subset of Observational Medical Outcomes Partnership (OMOP) CDM data fields. We investigated the empirical data model by annotating multi-institutional clinical data requests in free text and comparing the distributions of data model fields. The similar distribution of the entity mention types from two different sites indicates that the data model is generalizable for multi-institutional cohort identification queries.
关于将文本查询映射到公共数据模型
电子健康记录(EHRs)的广泛采用使数据驱动的方法能够用于临床护理和研究。然而,由于缺乏跨机构电子病历数据的句法和语义互操作性,这些方法的性能和可泛化性受到严重阻碍。为了解决这一问题,可以使用公共数据模型(cdm)对临床数据存储库中的临床数据进行标准化。在本文中,我们描述了从患者级信息检索查询到观察性医疗结果伙伴关系(OMOP) CDM数据字段的经验子集的实体提及类型的映射。我们通过在自由文本中标注多机构临床数据请求并比较数据模型字段的分布来研究经验数据模型。来自两个不同站点的实体提及类型的相似分布表明,该数据模型可用于多机构队列识别查询。
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信