BMC Medical Informatics and Decision Making · 2018 · 12 citations · 22 references
ComputingBiomedical InformaticsHealth InformaticsClinical InformaticsContainerizationHealth Data InteroperabilityContainer ArchitectureContainer ImagesContainer IsolationApplication ContainerizationContainer-based ArchitectureContainer-based DeploymentContainerized DeploymentDeploymentDocker ContainerizationHealthcare InstitutionsUpgrade Architecture
BACKGROUND: Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at over 200 healthcare institutions for querying patient data. The i2b2 platform has several components with numerous dependencies and configuration parameters, which renders the task of installing or upgrading i2b2 a challenging one. Even with the availability of extensive documentation and tutorials, new users often require several weeks to correctly install a functional i2b2 platform. The goal of this work is to simplify the installation and upgrade process for i2b2. Specifically, we have containerized the core components of the platform, and evaluated the containers for ease of installation. RESULTS: We developed three Docker container images: WildFly, database, and web, to encapsulate the three major deployment components of i2b2. These containers isolate the core functionalities of the i2b2 platform, and work in unison to provide its functionalities. Our evaluations indicate that i2b2 containers function successfully on the Linux platform. Our results demonstrate that the containerized components work out-of-the-box, with minimal configuration. CONCLUSIONS: Containerization offers the potential to package the i2b2 platform components into standalone executable packages that are agnostic to the underlying host operating system. By releasing i2b2 as a Docker container, we anticipate that users will be able to create a working i2b2 hive installation without the need to download, compile, and configure individual components that constitute the i2b2 cells, thus making this platform accessible to a greater number of institutions.
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An introduction to Docker for reproducible research
Carl Boettiger · ACM SIGOPS Operating Systems Review · 2015 · 1.1K citations · Full text
Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome
Daniel Garijo, Sarah Kinnings, Li Xie et al. · PLoS ONE · 2013 · 128 citations · Full text