prediction of antimicrobial resistance to ciprofloxacin based on patients' electronic medical recored
This code was written as part of a research on antimicrobial resistance. link to preprint(will be replaced once published).
Data are proprietary but can be made available upon reasonable request from the authors.
The input data are csv files paths with numerical features. I used 2 version: bactria gnostic and bacteria agnostic.
csv_paths= {
'Agnostic': get_script_dir() / 'agnostics_22_06_22.csv',
'Gnostic': get_script_dir() / 'gnostics_22_06_22.csv',
}
The model an ensemble of LASSO penalized logistic regression, random forest, gradient-boosted trees , and a simple neural network, stacked as described in Van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super learner. Statistical applications in genetics and molecular biology, 6(1). The code contains a workaround of an issue with other ensemble packages that can't deal with time series split (while cross validating to create the z_matrix the number of predictions is smaller than the whole data set because the first fold is used for training only). More over, the code plots AUC-ROC, calibration plot, SHAPley values, and net benefit plots, and saves a csv file with results.
described in a requirements.txt file
After updating the csv path to the data, just run:
python main.py for the creation of the search space, creation of z-table and training. after it's done, run python inference.py. In the end, run python calc_shap.py
MIT License