Package: PheNorm 0.1.1

Clara-Lea Bonzel

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>.

Authors:Sheng Yu [aut], Victor Castro [aut], Clara-Lea Bonzel [aut, cre], Molei Liu [aut], Chuan Hong [aut], Tianxi Cai [aut], PARSE LTD [aut]

PheNorm_0.1.1.tar.gz
PheNorm_0.1.1.zip(r-4.5)PheNorm_0.1.1.zip(r-4.4)PheNorm_0.1.1.zip(r-4.3)
PheNorm_0.1.1.tgz(r-4.4-any)PheNorm_0.1.1.tgz(r-4.3-any)
PheNorm_0.1.1.tar.gz(r-4.5-noble)PheNorm_0.1.1.tar.gz(r-4.4-noble)
PheNorm_0.1.1.tgz(r-4.4-emscripten)PheNorm_0.1.1.tgz(r-4.3-emscripten)
PheNorm.pdf |PheNorm.html
PheNorm/json (API)

# Install 'PheNorm' in R:
install.packages('PheNorm', repos = c('https://celehs.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/celehs/phenorm/issues

On CRAN:

1 exports 5 stars 1.59 score 0 dependencies 2 mentions 7 scripts 162 downloads

Last updated 4 years agofrom:7ea2ecee5b. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-winOKAug 20 2024
R-4.5-linuxOKAug 20 2024
R-4.4-winOKAug 20 2024
R-4.4-macOKAug 20 2024
R-4.3-winOKAug 20 2024
R-4.3-macOKAug 20 2024

Exports:PheNorm.Prob

Dependencies:

Example on simulated dataset

Rendered fromexample.Rmdusingknitr::rmarkdownon Aug 20 2024.

Last update: 2021-02-20
Started: 2021-02-20