MLD3 / JCO_CCI_aGVHD_prediction

Predicting Acute Graft-versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data from Electronic Health Records (Tang et al.), JCO Clinical Cancer Informatics 2020. https://doi.org/10.1200/CCI.19.00105

JCO CCI - aGVHD_prediction

Overview

• This is the code repository accompanying the manuscript "Predicting Acute Graft-versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data from Electronic Health Records".
• Authors: Shengpu Tang, Grant Chappell, Amanda Mazzoli, Muneesh Tewari, Sung Won Choi*, and Jenna Wiens*

*: senior authors of equal contribution

Dependencies

See: requirements.txt

• python 3.7.4
• pip packages
  • numpy 1.17.3
  • pandas 0.23.4
  • tsfresh 0.13.0
  • scipy 1.3.1
  • scikit-learn 0.21.3
  • matplotlib 3.1.1
  • seaborn 0.9.0
  • tqdm 4.36.1
  • joblib 0.14.0

Data

We provide anonymized patient data used in this study, including the static variables, vital sign data, and the outcome labels. A detailed description of the data can be found in the paper and the supplemental materials.

By downloading and using the content in this repository, you agree to comply with the following guiding principles for accessing authors’ original data and code:

1. I will acknowledge the authors (Tang, et al.) of this study in future publications featuring the dataset or code that has been made available to me.
2. I may not make any attempt to identify or contact individuals whose individual-level information is contained in the dataset entrusted to me.
3. I am responsible for all misuses and inappropriate disclosures made by me or by my study team.