PheNorm: Unsupervised Gold-Standard Label Free Phenotyping Algorithm for EHR Data

The algorithm combines the most predictive variable, such as count of the main International Classification of Diseases (ICD) codes, and other Electronic Health Record (EHR) features (e.g. health utilization and processed clinical note data), to obtain a score for accurate risk prediction and disease classification. In particular, it normalizes the surrogate to resemble gaussian mixture and leverages the remaining features through random corruption denoising. Background and details about the method can be found at Yu et al. (2018) <doi:10.1093/jamia/ocx111>.

Version: 0.1.0
Suggests: knitr, rmarkdown, testthat
Published: 2021-01-07
Author: Sheng Yu [aut], Victor Castro [aut], Clara-Lea Bonzel [aut, cre], Molei Liu [aut], Chuan Hong [aut], Tianxi Cai [aut], PARSE LTD [aut]
Maintainer: Clara-Lea Bonzel <clbonzel at hsph.harvard.edu>
BugReports: https://github.com/celehs/PheNorm/issues
License: GPL-3
URL: https://github.com/celehs/PheNorm
NeedsCompilation: no
Materials: README
CRAN checks: PheNorm results

Downloads:

Reference manual: PheNorm.pdf
Vignettes: my-vignette
Package source: PheNorm_0.1.0.tar.gz
Windows binaries: r-devel: PheNorm_0.1.0.zip, r-release: PheNorm_0.1.0.zip, r-oldrel: PheNorm_0.1.0.zip
macOS binaries: r-release (arm64): PheNorm_0.1.0.tgz, r-release (x86_64): PheNorm_0.1.0.tgz, r-oldrel: PheNorm_0.1.0.tgz

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