Graphical Modeling for HAPI

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:

1. Install the package "causallearn" in Python;
2. Relace the original "cit.py" file in the package with the new file in this repository;
3. Run "maggic.R" in R to generate synthetic datasets;
4. Run "mag_val.py".

cit.py: new file for conditional independence test including implementation of predictive permutative conditional independence test.

EuroCIM2023

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).