/xlungs-trustworthy-los-prediction

Trustworthy LoS Prediction Based on Multi-modal Data (AIME 2023)

Primary LanguageR

xlungs-trustworthy-los-prediction

Part of the xLUNGS project. This directory contains code and the TLOS dataset associated with the article

H. Baniecki, B. Sobieski, P. Bombiński, 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. https://arxiv.org/abs/2303.09817

 @inproceedings{baniecki2023hospital,
    title     = {{Hospital Length of Stay Prediction Based on Multi-modal Data 
                  towards Trustworthy Human-AI Collaboration in Radiomics}},
    author    = {Hubert Baniecki and Bartlomiej Sobieski and Przemysław Bombiński 
                 and Patryk Szatkowski and Przemysław Biecek},
    booktitle = {International Conference on Artificial Intelligence in Medicine},
    year      = {2023}
}

Data and code

The preprocessed TLOS dataset in the tabular form is available in data under the CC-BY-NC-ND-4.0 license. Raw X-ray data including images and radiology reports cannot be openly shared due to privacy concerns. For inquires, please write at xlungs@pw.edu.pl.

Exemplary code for performing the described analysis is available in code under the MIT license. It is accompanied by the key metadata checkpoints in results. For inquires, please write at h.baniecki@uw.edu.pl.

Dependencies

The analysis was performed using the following software:

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.