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

Documentation:

Reference manual:	PheCAP.pdf
Vignettes:	NER using MetaMAP Running NLP using NILE Example 1: Simulated Data Example 2: Real EHR Data Main Steps

Downloads:

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 (arm64): PheCAP_1.2.1.tgz, r-oldrel (arm64): PheCAP_1.2.1.tgz, r-release (x86_64): PheCAP_1.2.1.tgz, r-oldrel (x86_64): PheCAP_1.2.1.tgz
Old sources:	PheCAP archive

Linking:

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