Package: PheNorm 0.1.1
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:
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')) |
Bug tracker:https://github.com/celehs/phenorm/issues
Last updated 4 years agofrom:7ea2ecee5b. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Aug 20 2024 |
R-4.5-win | OK | Aug 20 2024 |
R-4.5-linux | OK | Aug 20 2024 |
R-4.4-win | OK | Aug 20 2024 |
R-4.4-mac | OK | Aug 20 2024 |
R-4.3-win | OK | Aug 20 2024 |
R-4.3-mac | OK | Aug 20 2024 |
Exports:PheNorm.Prob
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Fit the phenotyping algorithm PheNorm using EHR features | PheNorm.Prob |