Interpretable machine learning for time-to-event prediction in medicine and healthcare
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916491245387776 |
|---|---|
| author | Baniecki, Hubert Sobieski, Bartlomiej Szatkowski, Patryk Bombinski, Przemyslaw Biecek, Przemyslaw |
| author_facet | Baniecki, Hubert Sobieski, Bartlomiej Szatkowski, Patryk Bombinski, Przemyslaw Biecek, Przemyslaw |
| contents | Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challenges. To facilitate a comprehensive explanatory analysis of survival models, we formally introduce time-dependent feature effects and global feature importance explanations. We show how post-hoc interpretation methods allow for finding biases in AI systems predicting length of stay using a novel multi-modal dataset created from 1235 X-ray images with textual radiology reports annotated by human experts. Moreover, we evaluate cancer survival models beyond predictive performance to include the importance of multi-omics feature groups based on a large-scale benchmark comprising 11 datasets from The Cancer Genome Atlas (TCGA). Model developers can use the proposed methods to debug and improve machine learning algorithms, while physicians can discover disease biomarkers and assess their significance. We hope the contributed open data and code resources facilitate future work in the emerging research direction of explainable survival analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_09817 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Interpretable machine learning for time-to-event prediction in medicine and healthcare Baniecki, Hubert Sobieski, Bartlomiej Szatkowski, Patryk Bombinski, Przemyslaw Biecek, Przemyslaw Computer Vision and Pattern Recognition Applications Time-to-event prediction, e.g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications. However, only a few interpretable machine learning methods comply with its challenges. To facilitate a comprehensive explanatory analysis of survival models, we formally introduce time-dependent feature effects and global feature importance explanations. We show how post-hoc interpretation methods allow for finding biases in AI systems predicting length of stay using a novel multi-modal dataset created from 1235 X-ray images with textual radiology reports annotated by human experts. Moreover, we evaluate cancer survival models beyond predictive performance to include the importance of multi-omics feature groups based on a large-scale benchmark comprising 11 datasets from The Cancer Genome Atlas (TCGA). Model developers can use the proposed methods to debug and improve machine learning algorithms, while physicians can discover disease biomarkers and assess their significance. We hope the contributed open data and code resources facilitate future work in the emerging research direction of explainable survival analysis. |
| title | Interpretable machine learning for time-to-event prediction in medicine and healthcare |
| topic | Computer Vision and Pattern Recognition Applications |
| url | https://arxiv.org/abs/2303.09817 |