ArticleIEEE journal of biomedical and health informatics2014
Using common table expressions to build a scalable Boolean query generator for clinical data warehouses.
Article in IEEE journal of biomedical and health informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Empowering clinical trial design with agentic intelligence and real-world data.Nature communications · 2026Article
- Characterizing Basic and Complex Usage of i2b2 at an Academic Medical Center.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Using Closure Tables to Enable Cross-Querying of Ontologies in Database-Driven Applications.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2017Article
- i2b2t2: Unlocking Visualization for Clinical Research.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- Employing complex polyhierarchical ontologies and promoting interoperability of i2b2 data systems.AMIA ... Annual Symposium proceedings. AMIA SymposiumArticle
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Authors and funding
5 authors.
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Abstract
We present a custom, Boolean query generator utilizing common-table expressions (CTEs) that is capable of scaling with big datasets. The generator maps user-defined Boolean queries, such as those interactively created in clinical-research and general-purpose healthcare tools, into SQL. We demonstrate the effectiveness of this generator by integrating our study into the Informatics for Integrating Biology and the Bedside (i2b2) query tool and show that it is capable of scaling. Our custom generator replaces and outperforms the default query generator found within the Clinical Research Chart cell of i2b2. In our experiments, 16 different types of i2b2 queries were identified by varying four constraints: date, frequency, exclusion criteria, and whether selected concepts occurred in the same encounter. We generated nontrivial, random Boolean queries based on these 16 types; the corresponding SQL queries produced by both generators were compared by execution times. The CTE-based solution significantly outperformed the default query generator and provided a much more consistent response time across all query types (M = 2.03, SD = 6.64 versus M = 75.82, SD = 238.88 s). Without costly hardware upgrades, we provide a scalable solution based on CTEs with very promising empirical results centered on performance gains. The evaluation methodology used for this provides a means of profiling clinical data warehouse performance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.