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Ensembling neural networks is a long-standing technique for improving thegeneralization error of neural networks by combining networks with orthogonalproperties via a committee decision. We show that this technique is an idealfit for machine learning on medical data: First, ensembles are amenable toparallel and asynchronous learning, thus enabling efficient training ofpatient-specific component neural networks. Second, building on the idea ofminimizing generalization error by selecting uncorrelated patient-specificnetworks, we show that one can build an ensemble of a few selectedpatient-specific models that outperforms a single model trained on much largerpooled datasets. Third, the non-iterative ensemble combination step is anoptimal low-dimensional entry point to apply output perturbation to guaranteethe privacy of the patient-specific networks. We exemplify our framework ofdifferentially private ensembles on the task of early prediction of sepsis,using real-life intensive care unit data labeled by clinical experts.

Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis