XGBoost ML vs 6 TCH Clincians on ESRD & Death prediction, for the publication "Machine learning improves upon clinicians’ prediction of end stage renal failure" doi:10.3389/fmed.2022.837232
To get a local copy up and running follow these simple steps.
This is an example of how to list things you need to use the software and how to install them.
- Rstudio
- Clinical data in the form of tab-separated value files. There is no example data for this since these are medical records, but if you send me an email, I will give you the requisite format of fields and filenames.
- Clone the repo
git clone https://github.com/catlyst/trendal.git
Open the Rmarkdown (.Rmd) file within Rstudio and execute it via "Preview Notebook" or "Knit to HTML/PDF/Word". Its requisite R/Bioconductor libraries will be autoinstalled by the initial block of code.
See the open issues for a list of proposed features (and known issues).
At the moment this is restricted code being developed at our lab, but should you like to make any contributions, feature requests, bug reports or suggestions, they will be much appreciated.
Distributed under the MIT License. See LICENSE for more information.
Aaron Chuah - aaron.chuah@anu.edu.au @catlyst
