This is the repository for the projects on graphical modeling for HAPI
We implement the experiments using the base Python package "causallearn" How to run the codes:
- Install the package "causallearn" in Python.
- Replace the original "cit.py" file in the package with the new file in this repository;
- Run "maggic.R" in R to generate synthetic datasets;
- Run "mag_val.py".
cit.py: new file for conditional independence test, including predictive permutative conditional independence test implementation.
Supporting materials and references for the HAPI Poster in EuroCIM2023
Reference List for the Poster
[1] Najmanova, Klara, et al. "Risk factors for hospital-acquired pressure injury in patients with spinal cord injury during first rehabilitation: prospective cohort study." Spinal Cord 60.1 (2022): 45-52.
[2] Watson, David S., and Marvin N. Wright. "Testing conditional independence in supervised learning algorithms." Machine Learning 110.8 (2021): 2107-2129.
[3] Rich, Jonathan D., et al. "Meta‐Analysis Global Group in Chronic (MAGGIC) heart failure risk score: validation of a simple tool for the prediction of morbidity and mortality in heart failure with preserved ejection fraction." Journal of the American Heart Association 7.20 (2018): e009594.
[4] Scheel-Sailer, Anke, et al. "Risk Constellation Of Hospital Acquired Pressure Injuries In Patients With A Spinal Cord Injury/Disorder-Focus On Time Since Spinal Cord Injury/Disorder And Patients’ Age." (2022).
This study was partially funded by the Schweizer Paraplegiker Stiftung (SPS) and the ETH Zürich Foundation through the 2021-HS-348 ETH-SPS Digital Transformation in Personalized Healthcare for SCI.