PheCAP: High-Throughput Phenotyping with EHR using a Common Automated Pipeline (original) (raw)
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 |
| DOI: | 10.32614/CRAN.package.PheCAP |
| Author: | Yichi Zhang [aut], Chuan Hong [aut], Tianxi Cai [aut], PARSE LTD [aut, cre] |
| Maintainer: | PARSE LTD |
| 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 |
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