eol-onc

Machine Learning Approaches to Predict Six-Month Mortality among Patients with Cancer

Code associated with the manuscript by Ravi B. Parikh, MD, MPP, Christopher Manz, MD, Corey Chivers, PhD, Susan H. Regli, PhD, Jennifer Braun, MHA, Michael Draugelis, Lynn M. Schuchter, MD, Lawrence N. Shulman, MD, Mitesh S. Patel, MD, MBA, Nina O’Connor, MD.

Copyright (c) 2019 University of Pennsylvania Health System, MIT License

Citation

@article{parikh2019machine,
  title={Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer},
  author={Parikh, Ravi B and Manz, Christopher and Chivers, Corey and Regli, Susan Harkness and Braun, Jennifer and Draugelis, Michael E and Schuchter, Lynn M and Shulman, Lawrence N and Navathe, Amol S and Patel, Mitesh S and others},
  journal={JAMA network open},
  volume={2},
  number={10},
  pages={e1915997--e1915997},
  year={2019},
  publisher={American Medical Association}
}

Build & Run

Replace /data/eol/eol-onc/ with the path to this project and run the following:

docker build -t eol .
docker run -d --rm -it -v /data/eol/eol-onc/:/data eol /bin/bash

Training

Connect to the running container by finding the container name with docker ps

docker exec -it <container name> /bin/bash

In the container, spin up training jobs with:

cd /data
source activate eol_paper
nohup python3 EoL_model_ONC_v1_2.py -f data/OutpatientONC_v1_1_enc_data_features.csv --n-iter 100 --k-cv 5 -m rf > rf_gs.out &
disown %1

nohup python3 EoL_model_ONC_v1_2.py -f data/OutpatientONC_v1_1_enc_data_features.csv --n-iter 100 --k-cv 5 -m gb > gb_gs.out &
disown %1