Informative Priors Improve the Reliability of Multimodal Clinical Data
  Classification
L. Julian Lechuga Lopez Tim G. J. Rudner Farah E. Shamout
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.