We lifted 3 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| giovannicina/selecting_ood_detector | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| get_ood_aucs_score_for_all_models | Ran | giovannicina/selecting_ood_detector/selecting_OOD_detector/utils/scores_metrics.py code served (permissive licence) · get_code("8cae60f143d86eca") |
| average_values_in_nested_dict | Not yet run | giovannicina/selecting_ood_detector/selecting_OOD_detector/utils/scores_metrics.py code served (permissive licence) · get_code("d39d7a095907e922") |
| load_novelty_estimator | Not yet run | giovannicina/selecting_ood_detector/selecting_OOD_detector/utils/model_training.py code served (permissive licence) · get_code("8e83d0c925dd10fe") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Detection of Out-of-Distribution (OOD) samples in real time is a crucial safety check for deployment of machine learning models in the medical field. Despite a growing number of uncertainty quantification techniques, there is a lack of evaluation guidelines on how to select OOD detection methods in practice. This gap impedes implementation of OOD detection methods for real-world applications. Here, we propose a series of practical considerations and tests to choose the best OOD detector for a specific medical dataset. These guidelines are illustrated on a real-life use case of Electronic Health Records (EHR). Our results can serve as a guide for implementation of OOD detection methods in clinical practice, mitigating risks associated with the use of machine learning models in healthcare.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2109.14885")
get_code_for_paper("2109.14885")
have("2109.14885")
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