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Scientific software engineering principles
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Create generic functions for common EHR data cleaning and preprocessing operations which can be shared with the community
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Produce functions for defining study exposures, covariates and clinical outcomes across datasets which can be maintained across research groups and reused across many research studies
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Create modules for logically grouping common EHR operations e.g. study population definitions or datasource manipulation to enable code maintainability
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Create tests for individual functions and modules to ensure the robustness and correctness of results
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Track changes in analytical code and phenotypt definitions using controlled clinical terminology terms by making use of a source code revision control system
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Use formal software engineering best-practices to document workflows and data manipulation operations
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Standardized analytical approaches
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Build and distribute libraries for common EHR data manipulation or statistical analysis and include sufficient detail (e.g. command line arguments) for all tools used
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Produce and annotate machine-readable EHR phenotyping algorithms that can be systematically curated and reused by the community
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Use Digital Object Identifiers (DOIs) for transforming research artifacts into shareable citable resources and cross-reference from research output
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Deposit research resources (e.g. algorithms, code) in open-access repositories or software scientific journals and cross-reference from research output
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Virtual machines can potentially be used to encapsulate the data, operating system, analytical software and algorithms used to generate a manuscript and where applicable can be made available for others to reproduce the analytical pipeline.
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Literate programming
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Encapsulate both logic and programming code using literate programming approaches and tools which ensure logic and underlying processing code coexist
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