Informative Priors Improve the Reliability of Multimodal Clinical Data Classification

Leopoldo Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout

Abstract:

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.