2019
DOI: 10.2196/13043
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Health Care and Precision Medicine Research: Analysis of a Scalable Data Science Platform

Abstract: Background Health care data are increasing in volume and complexity. Storing and analyzing these data to implement precision medicine initiatives and data-driven research has exceeded the capabilities of traditional computer systems. Modern big data platforms must be adapted to the specific demands of health care and designed for scalability and growth. Objective The objectives of our study were to (1) demonstrate the implementation of a data science platform built on o… Show more

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Cited by 77 publications
(49 citation statements)
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“…First, electronic health records (EHRs) often do not have native functionality to integrate complex machine learning models. Significant investment in infrastructure is required [ 6 - 8 ]. Second, even after a model is initially implemented, machine learning models can incur substantial ongoing maintenance costs [ 9 - 11 ].…”
Section: Introductionmentioning
confidence: 99%
“…First, electronic health records (EHRs) often do not have native functionality to integrate complex machine learning models. Significant investment in infrastructure is required [ 6 - 8 ]. Second, even after a model is initially implemented, machine learning models can incur substantial ongoing maintenance costs [ 9 - 11 ].…”
Section: Introductionmentioning
confidence: 99%
“…Patient demographics, summarized medical histories, vital signs, and laboratory results available during the ED encounter were extracted from our local Observational Medical Outcomes Partnership (OMOP) data repository and analyzed within our computational health platform 6,7 . OMOP is a common data model for electronic health records in widespread use that leverages data standards to facilitate large‐scale systematic analyses.…”
Section: Methodsmentioning
confidence: 99%
“…From 1 November 2016 until 28 February 2019, all transfusion data on adults aged 18 years and older were extracted from our clinical data warehouse and data analysis platform, which captures and merges data from our electronic health record and blood bank management systems, Epic (Epic Corporation, Verona, WI) and SoftBank (SCC Soft Computer, Clearwater, FL), respectively (McPadden et al , ). Data were collected as part of an ongoing quality assurance review with no specific inclusion or exclusion criteria, other than patient age.…”
Section: Methodsmentioning
confidence: 99%