Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2411.00190 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916470735241216 |
|---|---|
| author | van Schaik, Tempest A. Liu, Xinggang Atallah, Louis Badawi, Omar |
| author_facet | van Schaik, Tempest A. Liu, Xinggang Atallah, Louis Badawi, Omar |
| contents | This work proposes a fairness monitoring approach for machine learning models that predict patient mortality in the ICU. We investigate how well models perform for patient groups with different race, sex and medical diagnoses. We investigate Documentation bias in clinical measurement, showing how fairness analysis provides a more detailed and insightful comparison of model performance than traditional accuracy metrics alone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00190 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Monitoring fairness in machine learning models that predict patient mortality in the ICU van Schaik, Tempest A. Liu, Xinggang Atallah, Louis Badawi, Omar Machine Learning Artificial Intelligence This work proposes a fairness monitoring approach for machine learning models that predict patient mortality in the ICU. We investigate how well models perform for patient groups with different race, sex and medical diagnoses. We investigate Documentation bias in clinical measurement, showing how fairness analysis provides a more detailed and insightful comparison of model performance than traditional accuracy metrics alone. |
| title | Monitoring fairness in machine learning models that predict patient mortality in the ICU |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2411.00190 |