Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond

• URL: http://arxiv.org/abs/2311.17133v1
• Date: Tue, 28 Nov 2023 18:15:53 GMT
• Title: Deployment of a Robust and Explainable Mortality Prediction Model: The COVID-19 Pandemic and Beyond
• Authors: Jacob R. Epifano, Stephen Glass, Ravi P. Ramachandran, Sharad Patel, Aaron J. Masino, Ghulam Rasool
• Abstract summary: This study investigated the performance, explainability, and robustness of deployed artificial intelligence (AI) models in predicting mortality during the COVID-19 pandemic and beyond.
• Score: 0.59374762912328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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