ELII: A novel inverted index for fast temporal query, with application to a large Covid-19 EHR dataset.
J Biomed Inform
; 117: 103744, 2021 05.
Article
in English
| MEDLINE | ID: covidwho-1155518
ABSTRACT
Fast temporal query on large EHR-derived data sources presents an emerging big data challenge, as this query modality is intractable using conventional strategies that have not focused on addressing Covid-19-related research needs at scale. We introduce a novel approach called Event-level Inverted Index (ELII) to optimize time trade-offs between one-time batch preprocessing and subsequent open-ended, user-specified temporal queries. An experimental temporal query engine has been implemented in a NoSQL database using our new ELII strategy. Near-real-time performance was achieved on a large Covid-19 EHR dataset, with 1.3 million unique patients and 3.76 billion records. We evaluated the performance of ELII on several types of queries classical (non-temporal), absolute temporal, and relative temporal. Our experimental results indicate that ELII accomplished these queries in seconds, achieving average speed accelerations of 26.8 times on relative temporal query, 88.6 times on absolute temporal query, and 1037.6 times on classical query compared to a baseline approach without using ELII. Our study suggests that ELII is a promising approach supporting fast temporal query, an important mode of cohort development for Covid-19 studies.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Information Storage and Retrieval
/
Electronic Health Records
/
Big Data
/
COVID-19
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Biomed Inform
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
J.jbi.2021.103744
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