  A Clinical Decision Support System (CDSS) is designed to enhance clinician
decision-making by combining system-generated recommendations with medical
expertise. Given the high costs, intensive labor, and time-sensitive nature of
medical treatments, there is a pressing need for efficient decision support,
especially in complex emergency scenarios. In these scenarios, where
information can be limited, an advanced CDSS framework that leverages AI
(artificial intelligence) models to effectively reduce diagnostic uncertainty
has utility. Such an AI-enabled CDSS framework with quantified uncertainty
promises to be practical and beneficial in the demanding context of real-world
medical care. In this study, we introduce the concept of Medical Entropy,
quantifying uncertainties in patient outcomes predicted by neural machine
translation based on the ICD-9 code of procedures. Our experimental results not
only show strong correlations between procedure and diagnosis sequences based
on the simple ICD-9 code but also demonstrate the promising capacity to model
trends of uncertainties during hospitalizations through a data-driven approach.
