kesernetwork
Visualization of the KESER Network
A shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021)
kesernetwork
Overview
The kesernetwork builds a shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts.
Installation
Install the released version of kesernetwork from CRAN:
install.packages("kesernetwork")
Or install the development version from GitHub with:
install.packages("remotes")
remotes::install_github("celehs/kesernetwork")
Usage
This is a basic example which shows you how to run the kesernetwork
app. Remember you need to get access to the data and save it to your
local computer. In order to guarantee some dependencies are loaded, you
must use library(kesernetwork) beforehand, instead of directly running
kesernetwork::run_app().
library(kesernetwork)
run_app(Rdata_path = "path/to/kesernetwork.RData")
See the getting started guide to learn how to use kesernetwork.
Citations
- Hong, C., Rush, E., Liu, M. et al. Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data. npj Digit. Med. 4, 151 (2021). https://doi.org/10.1038/s41746-021-00519-z