Big Data for Quality of Life (BD4QoL) EU project repository at the Oslo Centre for Biostatistics and Epidemiology. This repository contains the code used for developing prediction models in the BD4QoL using the restrospective data from University of Milano (UMIL), University Medical Center Mainz, and University of Bristol.
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All model training and statistics are located in the folder
src. -
SLURMshell scripts can be found injobs. -
List of variables used for different analyses are found in
data. -
Naming rules
- Scripts used for data cleaning and preparation start with the prefix
A, e.g.A1_preprocessing.R. - Scripts used to train models start with the letter
M - Post-processing and statistics scripts start with the letter
E - Functions and classes used in several other scripts are in
functions.py
- Scripts used for data cleaning and preparation start with the prefix
BD4QoL/
├── data/
│ ├── all_variables.csv
│ ├── prospective_variables.csv
│ ├── encoded_vars.csv
│ ├── propensity_variables.csv
│ └── bootstrap_ids.csv
├── src/
│ ├── __init__.py
│ ├── A1_preprocessing.R
│ ├── A2_bootstrap_resample.R
│ ├── E1_bootstrap_statistics.py
│ ├── E2_make_figures.py
│ ├── M1_logreg.py
│ ├── M2_xgboost.py
│ ├── M3_survival.py
│ ├── M4_xgboost_conformal.py
│ ├── M5_xgb_conformal_parallel.py
│ ├── M6_logreg_decline.py
│ ├── M7_lasso_conformal.py
│ ├── M8_lasso_conformal_parallel.py
│ ├── M9_survival_parallel.py
│ ├── M10_propensity.py
│ ├── M11_propensity_parallel.py
│ └── functions.py
├── jobs/
│ ├── M4_xgboost_conformal.sh
│ ├── M5_xgb_conformal_parallel_complete.sh
│ ├── M6_logreg_decline.sh
│ ├── M7_ols_lasso.sh
│ ├── M8_lasso_conformal_parallel.sh
│ ├── M5_commands.conf
│ └── M8_commands.conf
├── LICENSE
├── README.md
└── requirements.txt
The models were developed with python 3.10 and the following requirements:
scikit-learn==1.3.1
xgboost==2.0.0
pandas==2.1.0
seaborn==0.12.2
numpy==1.25.2
matplotlib==3.8.0
scipy==1.11.2
torch==2.0.1
jupyterlab
plotly
ipython
hyperopt==0.2.7
mapie==0.7.0