| Type: | Package |
| Title: | A Semi-Supervised Method for Prediction of Phenotype Event Times |
| Version: | 0.1.0-1 |
| Description: | A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data. |
| URL: | https://github.com/celehs/SAMGEP |
| BugReports: | https://github.com/celehs/SAMGEP/issues |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | stats, mvtnorm, nlme, pROC, abind, nloptr, foreach, doParallel, parallel, Rcpp |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| LazyData: | true |
| NeedsCompilation: | yes |
| Packaged: | 2021-01-04 02:54:21 UTC; yuriahuja |
| Author: | Yuri Ahuja [aut, cre], Tianxi Cai [aut], PARSE LTD [aut] |
| Maintainer: | Yuri Ahuja <Yuri_Ahuja@hms.harvard.edu> |
| Repository: | CRAN |
| Date/Publication: | 2021-01-06 10:00:02 UTC |
SAMGEP: A Semi-supervised Method for Prediction of Phenotype Event Times Using the Electronic Health Record
Description
Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP) is a novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data.
Semi-supervised Adaptive Markov Gaussian Process (SAMGEP)
Description
Semi-supervised Adaptive Markov Gaussian Process (SAMGEP)
Usage
samgep(
dat_train = NULL,
dat_test = NULL,
Cindices = NULL,
w = NULL,
w0 = NULL,
V = NULL,
observed = NULL,
nX = 10,
covs = NULL,
survival = FALSE,
Estep = Estep_partial,
Xtrain = NULL,
Xtest = NULL,
alpha = NULL,
r = NULL,
lambda = NULL,
surrIndex = NULL,
nCores = 1
)
Arguments
dat_train |
(optional if Xtrain is supplied) Raw training data set, including patient IDs (ID), healthcare utilization feature (H) and censoring time (C) |
dat_test |
(optional) Raw testing data set, including patient IDs (ID), a healthcare utilization feature (H) and censoring time (C) |
Cindices |
(optional if Xtrain is supplied) Column indices of EHR feature counts in dat_train/dat_test |
w |
(optional if Xtrain is supplied) Pre-optimized EHR feature weights |
w0 |
(optional if Xtrain is supplied) Initial (i.e. partially optimized) EHR feature weights |
V |
(optional if Xtrain is supplied) nFeatures x nEmbeddings embeddings matrix |
observed |
(optional if Xtrain is supplied) IDs of patients with observed outcome labels |
nX |
Number of embedding features (defaults to 10) |
covs |
(optional) Baseline covariates to include in model; not yet operational |
survival |
Binary indicator of whether target phenotype is of type survival (i.e. stays positive after incident event) or relapsing-remitting (defaults to FALSE) |
Estep |
E-step function to use (Estep_partial or Estep_full; defaults to Estep_partial) |
Xtrain |
(optional) Embedded training data set, including patient IDs (ID), healthcare utilization feature (H) and censoring time (C) |
Xtest |
(optional) Embedded testing data set, including patient IDs (ID), healthcare utilization feature (H) and censoring time (C) |
alpha |
(optional) Relative weight of semi-supervised to supervised MGP predictors in SAMGEP ensemble |
r |
(optional) Scaling factor of inter-temporal correlation |
lambda |
(optional) L1 regularization hyperparameter for feature weight (w) optimization |
surrIndex |
(optional) Index (within Cindices) of primary surrogate index for outcome event |
nCores |
Number of cores to use for parallelization (defaults to 1) |
Value
w_opt Optimized feature weights (w)
r_opt Optimized inter-temporal correlation scaling factor (r)
alpha_opt Optimized semi-supservised:supervised relative weight (alpha)
lambda_opt Optiized L1 regularization hyperparameter (lambda)
margSup Posterior probability predictions of supervised model (MGP Supervised)
margSemisup Posterior probability predictions of semi-supervised model (MGP Semi-supervised)
margMix Posterior probability predictions of SAMGEP
cumSup Cumulative probability predictions of supervised model (MGP Supervised)
cumSemisup Cumulative probability predictions of semi-supervised model (MGP Semi-supervised)
cumMix Cumulative probability predictions of SAMGEP
Simulated Dataset
Description
Click HERE to view details.
Usage
simdata
Format
An object of class list of length 3.
Examples
str(simdata)