Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma
- DOI
- 10.3389/frai.2022.1059033
- Published
- 2022-12-07
- Container
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/frai.2022.1059033,
title = {Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma},
author = {Du Wang and Sang Ho Lee and Huaizhi Geng and Haoyu Zhong and John Plastaras and Andrzej Wojcieszynski and Richard Caruana and Ying Xiao},
year = {2022},
journal = {Frontiers in Artificial Intelligence},
doi = {10.3389/frai.2022.1059033},
url = {https://doi.org/10.3389/frai.2022.1059033}
}RIS
TY - JOUR TI - Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma AU - Du Wang AU - Sang Ho Lee AU - Huaizhi Geng AU - Haoyu Zhong AU - John Plastaras AU - Andrzej Wojcieszynski AU - Richard Caruana AU - Ying Xiao PY - 2022 JO - Frontiers in Artificial Intelligence DO - 10.3389/frai.2022.1059033 UR - https://doi.org/10.3389/frai.2022.1059033 ER -
APA
Wang, D., Lee, S. H., Geng, H., Zhong, H., Plastaras, J., Wojcieszynski, A., Caruana, R., & Xiao, Y. (2022). Interpretable machine learning for predicting pathologic complete response in patients treated with chemoradiation therapy for rectal adenocarcinoma. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2022.1059033
Source records
- crossref · retrieved 2026-11-10T12:01:57.651Z