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) , Yu et al. (2017) , and Liao et al. (2015) .


Reference manual

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1.2.1 by PARSE LTD, a year ago

https://celehs.github.io/PheCAP/, https://github.com/celehs/PheCAP

Report a bug at https://github.com/celehs/PheCAP/issues

Browse source code at https://github.com/cran/PheCAP

Authors: Yichi Zhang [aut] , Chuan Hong [aut] , Tianxi Cai [aut] , PARSE LTD [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports graphics, methods, stats, utils, glmnet, RMySQL

Suggests ggplot2, e1071, randomForestSRC, xgboost, knitr, rmarkdown

See at CRAN