On MIMIC-III mortality and length-of-stay tasks, temporal evaluation shows raw-feature models lose up to 0.29 AUROC across the 2008 EHR switch, while expert-defined clinical concept features cut the drop to 0.06.
Automatic differentiation in pytorch
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Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
On MIMIC-III mortality and length-of-stay tasks, temporal evaluation shows raw-feature models lose up to 0.29 AUROC across the 2008 EHR switch, while expert-defined clinical concept features cut the drop to 0.06.