PheCAP: High-Throughput Phenotyping with EHR using a Common Automated Pipeline

Implement surrogate-assisted feature extraction (SAFE) and common machine learning approaches to train and validate phenotyping models. Background and details about the methods can be found at Zhang et al. (2019) <doi:10.1038/s41596-019-0227-6>, Yu et al. (2017) <doi:10.1093/jamia/ocw135>, and Liao et al. (2015) <doi:10.1136/bmj.h1885>.

Version: 1.2.1
Depends: R (≥ 3.3.0)
Imports: graphics, methods, stats, utils, glmnet, RMySQL
Suggests: ggplot2, e1071, randomForestSRC, xgboost, knitr, rmarkdown
Published: 2020-09-17
Author: Yichi Zhang [aut], Chuan Hong [aut], Tianxi Cai [aut], PARSE LTD [aut, cre]
Maintainer: PARSE LTD <software at parse-health.org>
BugReports: https://github.com/celehs/PheCAP/issues
License: GPL-3
URL: https://celehs.github.io/PheCAP/, https://github.com/celehs/PheCAP
NeedsCompilation: no
Materials: README
CRAN checks: PheCAP results

Downloads:

Reference manual: PheCAP.pdf
Vignettes: NER using MetaMAP
Running NLP using NILE
Example 1: Simulated Data
Example 2: Real EHR Data
Main Steps
Package source: PheCAP_1.2.1.tar.gz
Windows binaries: r-devel: PheCAP_1.2.1.zip, r-release: PheCAP_1.2.1.zip, r-oldrel: PheCAP_1.2.1.zip
macOS binaries: r-release: PheCAP_1.2.1.tgz, r-oldrel: PheCAP_1.2.1.tgz
Old sources: PheCAP archive

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