The Intensive Care Unit (ICU) is a hospital department where machine learning
has the potential to provide valuable assistance in clinical decision making.
Classical machine learning models usually only provide point-estimates and no
uncertainty of predictions. In practice, uncertain predictions should be
presented to doctors with extra care in order to prevent potentially
catastrophic treatment decisions. In this work we show how Bayesian modelling
and the predictive uncertainty that it provides can be used to mitigate risk of
misguided prediction and to detect out-of-domain examples in a medical setting.
We derive analytically a bound on the prediction loss with respect to
predictive uncertainty. The bound shows that uncertainty can mitigate loss.
Furthermore, we apply a Bayesian Neural Network to the MIMIC-III dataset,
predicting risk of mortality of ICU patients. Our empirical results show that
uncertainty can indeed prevent potential errors and reliably identifies
out-of-domain patients. These results suggest that Bayesian predictive
uncertainty can greatly improve trustworthiness of machine learning models in
high-risk settings such as the ICU.

本文介绍了如何使用贝叶斯建模和预测不确定性来减少误判和检测出领域外的医疗样本，通过一个贝叶斯神经网络应用于 MIMIC-III 数据集，展示了它在 ICU 患者的死亡风险预测中防止潜在误差和可靠地确定领域外患者的作用，证明了贝叶斯预测不确定性在高风险环境下机器学习模型可靠性的重要性。