interpret-time-to-event

This repository contains supplementary data and code associated with the article:

H. Baniecki, B. Sobieski, P. Szatkowski, P. Bombinski, P. Biecek. Interpretable machine learning for time-to-event prediction in medicine and healthcare. Artificial Intelligence in Medicine, 2024

@article{baniecki2025interpretable,
    title = {Interpretable machine learning for time-to-event prediction in medicine and healthcare},
    author = {Hubert Baniecki and Bartlomiej Sobieski and Patryk Szatkowski and Przemyslaw Bombinski and Przemyslaw Biecek},
    journal = {Artificial Intelligence in Medicine},
    volume = {159},
    pages = {103026},
    year = {2025},
    doi = {https://doi.org/10.1016/j.artmed.2024.103026}
}

Directory xlungs-trustworthy-los-prediction contains an updated code and data from the GitHub repository supplementing the initial version of the paper:

H. Baniecki, B. Sobieski, P. Bombinski, P. Szatkowski, P. Biecek. Hospital Length of Stay Prediction Based on Multi-modal Data towards Trustworthy Human-AI Collaboration in Radiomics. International Conference on Artificial Intelligence in Medicine, 2023

Acknowledgements

This work was financially supported by the Polish National Center for Research and Development grant number INFOSTRATEG-I/0022/2021-00, and carried out with the support of the Laboratory of Bioinformatics and Computational Genomics and the High Performance Computing Center of the Faculty of Mathematics and Information Science, Warsaw University of Technology.