ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
A Method for EHR Phenotype Management in an i2b2 Data Warehouse.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- PheBee: A Graph-Aware System for Scalable, Traceable, and Semantic Phenotyping.medRxiv : the preprint server for health sciences · 2026Article
- Clinical Data Warehousing: A Scoping Review.Journal of the Society for Clinical Data Management · 2024Article
- Research, development, and evaluation of the practical effect of a storage inflow and outflow management system for consumables in the endocrinology department of a hospital.BMC medical informatics and decision making · 2022Article
- What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions.JMIR formative research · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Electronic health record (EHR) data is valuable for finding patients for clinical research and analytics but is complex to query. EHR phenotyping involves the curation and dissemination of best practices for querying commonly studied populations. Phenotyping software computes patterns in clinical and administrative data and may add the found patterns as derived variables to a database that researchers can query. This paper describes a method for managing EHR phenotypes in a data warehouse as the warehouse is incrementally updated with new and changed data. We have implemented this method in proof-of-concept form as an extension to the Eureka! Clinical Analytics phenotyping software system and evaluated the implementation's performance. The method shows promise for realizing the efficient addition, modification, and removal of derived variables representing phenotypes in a data warehouse.
Identifiers
31258960PMC6568136What OpenQuestion holds
Registered trials
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.