Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis
| Autor: | Schamoni, Shigehiko, Hagmann, Michael, Riezler, Stefan |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the 7th Machine Learning for Healthcare Conference, PMLR 182:123-145, 2022 |
| Druh dokumentu: | Working Paper |
| Popis: | Ensembling neural networks is a long-standing technique for improving the generalization error of neural networks by combining networks with orthogonal properties via a committee decision. We show that this technique is an ideal fit for machine learning on medical data: First, ensembles are amenable to parallel and asynchronous learning, thus enabling efficient training of patient-specific component neural networks. Second, building on the idea of minimizing generalization error by selecting uncorrelated patient-specific networks, we show that one can build an ensemble of a few selected patient-specific models that outperforms a single model trained on much larger pooled datasets. Third, the non-iterative ensemble combination step is an optimal low-dimensional entry point to apply output perturbation to guarantee the privacy of the patient-specific networks. We exemplify our framework of differentially private ensembles on the task of early prediction of sepsis, using real-life intensive care unit data labeled by clinical experts. Comment: Accepted at MLHC 2022 |
| Databáze: | arXiv |
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