Deployment of a Robust and Explainable Mortality Prediction Model: The
  COVID-19 Pandemic and Beyond
Jacob R. Epifano Stephen Glass Ravi P. Ramachandran Sharad Patel Aaron J. Masino Ghulam Rasool
This study investigated the performance, explainability, and robustness of
deployed artificial intelligence (AI) models in predicting mortality during the
COVID-19 pandemic and beyond. The first study of its kind, we found that
Bayesian Neural Networks (BNNs) and intelligent training techniques allowed our
models to maintain performance amidst significant data shifts. Our results
emphasize the importance of developing robust AI models capable of matching or
surpassing clinician predictions, even under challenging conditions. Our
exploration of model explainability revealed that stochastic models generate
more diverse and personalized explanations thereby highlighting the need for AI
models that provide detailed and individualized insights in real-world clinical
settings. Furthermore, we underscored the importance of quantifying uncertainty
in AI models which enables clinicians to make better-informed decisions based
on reliable predictions. Our study advocates for prioritizing implementation
science in AI research for healthcare and ensuring that AI solutions are
practical, beneficial, and sustainable in real-world clinical environments. By
addressing unique challenges and complexities in healthcare settings,
researchers can develop AI models that effectively improve clinical practice
and patient outcomes.