The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. While the huge amount of information in EHR can be used in clinical informatics tasks, data mining for EHR data with complex structure is challenging. Traditional data mining approaches require domain knowledge to select relevant codes, which hampers the efficiency for translational research.
This packages implements the Knowledge Extraction via Sparse Embedding Regression (KESER) algorithm from our working paper, which is currently available upon request. We provide functions to use large scale code embeddings to facilitate effective feature selection and knowledge discovery with EHR data. A main advantage of the proposed embedding-based method over the existing feature selection algorithms is that it can be performed based on only summary data that can be shared across research groups (see below for the workflow).
Install the KESER package from GitHub and then load it into R.
# install.packages("remotes")
remotes::install_github("celehs/KESER")
library(KESER)Load the clinical embeddings for the depression data example. One can
follow the instructions
HERE to
derive clinical embeddings from code-code coocurrence matrix.
dir <- "https://github.com/celehs/KESER/raw/master/rdata/"
data <- readRDS(url(paste0(dir, "depression.rds"), "rb"))To perform feature
selection,
the input data should be a list that consists of 6 elements:
X_full_lst, Y_full_lst, X_train_lst, Y_train_lst, X_valid_lst,
Y_valid_lst.
str(data)## List of 6
## $ X_full_lst :List of 2
## ..$ : num [1:3700, 1:387] -1.426 0.191 0.403 -0.141 -0.572 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:387] "CCS:130" "CCS:131" "CCS:156" "CCS:163" ...
## ..$ : num [1:5400, 1:552] -1.055 -0.919 0.183 -0.657 -0.427 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:552] "CCS:130" "CCS:131" "CCS:156" "CCS:163" ...
## $ Y_full_lst :List of 2
## ..$ : num [1:3700] -1.298 0.097 -0.41 -0.118 -0.228 ...
## ..$ : num [1:5400] -0.698 -0.702 -0.7 -0.259 -0.126 ...
## $ X_train_lst:List of 2
## ..$ : num [1:3700, 1:387] 1.23 -3.84e-01 -8.46e-02 1.28e-15 4.63e-01 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:387] "CCS:130" "CCS:131" "CCS:156" "CCS:163" ...
## ..$ : num [1:5400, 1:552] -0.4631 -0.0193 0.461 -0.1689 0.2052 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:552] "CCS:130" "CCS:131" "CCS:156" "CCS:163" ...
## $ Y_train_lst:List of 2
## ..$ : num [1:3700] 1.36 2.85e-01 -6.31e-02 -7.11e-16 9.94e-01 ...
## ..$ : num [1:5400] -0.8208 -0.6993 0.7399 0.4333 0.0566 ...
## $ X_valid_lst:List of 2
## ..$ : num [1:3700, 1:387] -1.32 -4.20e-01 -5.76e-02 -1.07e-15 8.26e-01 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:387] "CCS:130" "CCS:131" "CCS:156" "CCS:163" ...
## ..$ : num [1:5400, 1:552] -1.051 -0.901 0.101 -0.688 -0.45 ...
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : NULL
## .. .. ..$ : chr [1:552] "CCS:130" "CCS:131" "CCS:156" "CCS:163" ...
## $ Y_valid_lst:List of 2
## ..$ : num [1:3700] -1.36 3.17e-01 -8.34e-02 2.64e-16 3.40e-01 ...
## ..$ : num [1:5400] -0.707 -0.707 -0.716 -0.157 -0.215 ...
- Large Scale Code Embedding with Applications to Feature Selection and Knowledge Discovery in Electronic Health Records. Working Paper.
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