Deploying clinical sepsis prediction software across five independent hospital networks revealed dramatic performance variance due to differing laboratory workflows and vital sign documentation frequencies. This paper examines defensive architectures.
Using autoencoder reconstruction error, the system identified when patient cohort characteristics diverged significantly from the training distribution, successfully quarantining unreliable inference outputs.
Automated local fine-tuning restored area under the ROC curve (AUROC) from 0.71 back to 0.89 while remaining within the pre-cleared FDA modification protocol.