sureLDA: A Novel Multi-Disease Automated Phenotyping Method for the EHR
A statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA <doi:10.1093/jamia/ocaa079> for details.
| Version: | 0.1.0-1 |
| Depends: | R (≥ 3.0),Matrix |
| Imports: | pROC,glmnet,MAP,Rcpp,foreach,doParallel |
| LinkingTo: | Rcpp,RcppArmadillo |
| Suggests: | knitr,rmarkdown |
| Published: | 2020-11-10 |
| DOI: | 10.32614/CRAN.package.sureLDA |
| Author: | Yuri Ahuja [aut, cre], Tianxi Cai [aut], PARSE LTD [aut] |
| Maintainer: | Yuri Ahuja <Yuri_Ahuja at hms.harvard.edu> |
| BugReports: | https://github.com/celehs/sureLDA/issues |
| License: | GPL-3 |
| URL: | https://github.com/celehs/sureLDA |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | sureLDA results |
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