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 |
Documentation:
Downloads:
Linking:
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https://CRAN.R-project.org/package=sureLDA
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