为大规模新冠EHR数据设计快速时序查询索引，提速千倍以上。
TELII: Temporal Event Level Inverted Indexing for Cohort Discovery on a Large Covid-19 EHR Dataset
- 构建事件级时间倒排索引，预存事件间时间差关系
- 在887万患者数据上实现毫秒级响应，最快快2000倍
- 适配多种EHR数据，助力临床研究快速验证假设
队列发现是电子健康记录（EHR）数据临床研究的关键步骤。时序查询在大规模EHR数据中常见但耗时且易出错。本文提出TELII，一种面向大规模EHR数据的时序事件级倒排索引方法，通过预先计算并存储事件间的关系及其时间差，实现快速准确的时序查询。我们在包含887万患者的OPTUM去标识化新冠EHR数据集上实现了TELII，使用MongoDB后端完成四种常见时序查询任务。结果表明，与现有非时序倒排索引相比，TELII的查询速度最高提升2000倍，响应时间达到毫秒级，使研究人员能快速探索事件关联并获得初步研究证据。TELII不仅易于实现且可迁移至其他EHR数据，具备作为EHR应用查询引擎的潜力，支持快速、精准、友好的查询体验。
原文摘要 · Abstract (English)
Cohort discovery is a crucial step in clinical research on Electronic Health Record (EHR) data. Temporal queries, which are common in cohort discovery, can be time-consuming and prone to errors when processed on large EHR datasets. In this work, we introduce TELII, a temporal event level inverted indexing method designed for cohort discovery on large EHR datasets. TELII is engineered to pre-compute and store the relations along with the time difference between events, thereby providing fast and accurate temporal query capabilities. We implemented TELII for the OPTUM de-identified COVID-19 EHR dataset, which contains data from 8.87 million patients. We demonstrate four common temporal query tasks and their implementation using TELII with a MongoDB backend. Our results show that the temporal query speed for TELII is up to 2000 times faster than that of existing non-temporal inverted indexes. TELII achieves millisecond-level response times, enabling users to quickly explore event relations and find preliminary evidence for their research questions. Not only is TELII practical and straightforward to implement, but it also offers easy adaptability to other EHR datasets. These advantages underscore TELII's potential to serve as the query engine for EHR-based applications, ensuring fast, accurate, and user-friendly query responses.
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