Abstract
BackgroundThere is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data. We implemented, described, and applied a cloud computing solution utilizing the fast health interoperability resources (FHIR) standardization and state-of-the-art parallel distributed computing platform for advanced analytics.MethodsWe utilized the architecture of the modern predictive analytics platform called Cerner® HealtheDataLab and described the suite of cloud computing services and Apache Projects that it relies on. We validated the platform by replicating and improving on a previous single pediatric institution study/model on readmission and developing a multi-center model of all-cause readmission for pediatric-age patients using the Cerner® Health Facts Deidentified Database (now updated and referred to as the Cerner Real World Data). We retrieved a subset of 1.4 million pediatric encounters consisting of 48 hospitals’ data on pediatric encounters in the database based on a priori inclusion criteria. We built and analyzed corresponding random forest and multilayer perceptron (MLP) neural network models using HealtheDataLab.ResultsUsing the HealtheDataLab platform, we developed a random forest model and multi-layer perceptron model with AUC of 0.8446 (0.8444, 0.8447) and 0.8451 (0.8449, 0.8453) respectively. We showed the distribution in model performance across hospitals and identified a set of novel variables under previous resource utilization and generic medications that may be used to improve existing readmission models.ConclusionOur results suggest that high performance, elastic cloud computing infrastructures such as the platform presented here can be used for the development of highly predictive models on EMR data in a secure and robust environment. This in turn can lead to new clinical insights/discoveries.
Highlights
There is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data
The application of predictive analytics and artificial intelligence (AI) in healthcare is met with several challenges including data access, standardization, collaboration, computing resource needs, and deployment of predictive models [1, 2]
We addressed the challenges of improving predictive analytics and AI in healthcare using the Cerner®
Summary
There is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data. The recent proliferation of big data in medicine [3] and improvements in high performance cloud computing [4] have necessitated the consideration of cloud computing for both storage and maintenance of data in medical/life science research as well as for business intelligence in general [5, 6]. Big data analyses require algorithms adapted to high performance parallel distributed computing. This has been addressed in the development and application of new methods in bioinformatics, statistical genetics, and in data science. These applications include analysis of structured data as well as medical images and genomic data [6, 8,9,10]
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