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The Myth of Complete AI-Fairness Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift

Authors : Miguel Rios, Ameen Abu-Hanna

Published in: Artificial Intelligence in Medicine

Publisher: Springer International Publishing

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Abstract

Neural models, with their ability to provide novel representations, have shown promising results in prediction tasks in healthcare. However, patient demographics, medical technology, and quality of care change over time. This often leads to drop in the performance of neural models for prospective patients, especially in terms of their calibration. The deep kernel learning (DKL) framework may be robust to such changes as it combines neural models with Gaussian processes, which are aware of prediction uncertainty. Our hypothesis is that out-of-distribution test points will result in probabilities closer to the global mean and hence prevent overconfident predictions. This in turn, we hypothesise, will result in better calibration on prospective data.
This paper investigates DKL’s behaviour when facing a temporal shift, which was naturally introduced when an information system that feeds a cohort database was changed. We compare DKL’s performance to that of a neural baseline based on recurrent neural networks. We show that DKL indeed produced superior calibrated predictions. We also confirm that the DKL’s predictions were indeed less sharp. In addition, DKL’s discrimination ability was even improved: its AUC was 0.746 \( (\pm \)0.014 std), compared to 0.739 (±0.028 std) for the baseline. The paper demonstrated the importance of including uncertainty in neural computing, especially for their prospective use.

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Metadata
Title
Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift
Authors
Miguel Rios
Ameen Abu-Hanna
Copyright Year
2021
Book
Artificial Intelligence in Medicine

Print ISBN: 978-3-030-77210-9

Electronic ISBN: 978-3-030-77211-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77211-6_22

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