R package for cleaning data from Electronic Health Record systems, focused on cleaning height and weight measurements.
This package implements the Daymont et al. algorithm, as specified in Supplemental File 3 within the Supplementary Material published with that paper.
Carrie Daymont, Michelle E Ross, A Russell Localio, Alexander G Fiks, Richard C Wasserman, Robert W Grundmeier, Automated identification of implausible values in growth data from pediatric electronic health records, Journal of the American Medical Informatics Association, Volume 24, Issue 6, November 2017, Pages 1080–1087, https://doi.org/10.1093/jamia/ocx037
This package also includes an R version of the SAS macro published by the CDC for calculating percentiles and Z-scores of pediatric growth observations and utilities for working with both functions. As of summer 2021, it also supports cleaning anthropometric measurements for adults up to age 65. The adult algorithm has not yet been published in a peer-reviewed publication, but is described in detail at Adult algorithm.
The growthcleanr package processes data prepared in a specific format
to identify biologically implausible height and weight measurements. It
bases these evaluations on techniques which use patient-specific
longitudinal analysis and variations from published growth trajectory
charts for pediatric subjects. These techniques are performed in a
specific order which refines and improves results throughout the
process.
Results from growthcleanr include a flag for each measurement
indicating whether it is to be included or excluded based on
plausibility, with a variety of specific types of exclusions identified
distinctly. These flags can be analyzed further by researchers studying
anthropometric EHR data to determine which measurements to include or
exclude in their own studies. No values are deleted or otherwise
removed; each is only flagged in a new column.
To start running growthcleanr, an R installation with a variety of
additional packages is required, as is a growth measurement dataset
prepared for use in growthcleanr.
The rest of this documentation includes:
- Quickstart, a brief tour of using growthcleanr, including data preparation
- Installation, examples of cleaning data, multiple options, example data
- Configuration options, changing growthcleanr operational settings
- Understanding growthcleanr output, the exclusion types growthcleanr identifies
- Adult algorithm, a detailed description of how growthcleanr assesses observations from adult subjects
- Computing BMI percentiles and Z-scores, additional functions for common data transforms and determining percentiles and Z-scores using the CDC method
- Working with large
datasets,
notes and suggestions for running
growthcleanrwith large data sources - Next steps, notes on potential enhancements to the pediatric and adult algorithms
For a detailed history of released versions, see the
Changelog
orNEWS.md. Tagged releases, starting with 1.2.3 in January 2021, are
listed at
GitHub.