Machine learning-aided clinical decision support has the potential to
significantly improve patient care. However, existing efforts in this domain
for principled quantification of uncertainty have largely been limited to
applications of ad-hoc solutions that do not consistently improve reliability.
In this work, we consider stochastic neural networks and design a tailor-made
multimodal data-driven (M2D2) prior distribution over network parameters. We
use simple and scalable Gaussian mean-field variational inference to train a
Bayesian neural network using the M2D2 prior. We train and evaluate the
proposed approach using clinical time-series data in MIMIC-IV and corresponding
chest X-ray images in MIMIC-CXR for the classification of acute care
conditions. Our empirical results show that the proposed method produces a more
reliable predictive model compared to deterministic and Bayesian neural network
baselines.

利用机器学习辅助临床决策支持有潜力显著改善患者护理，本研究设计了一种定制的多模态数据驱动的概率分布，并提出了一种简单可扩展的高斯平均场变分推理来训练具有该先验的贝叶斯神经网络，根据实验结果表明，该方法相比确定性和贝叶斯神经网络的基准模型产生了更可靠的预测模型。