Multimodal Bayesian Network for Robust Assessment of Casualty in Autonomous Triage

Mass Casualty Incidents (MCIs) can overwhelm emergency medical systems and resulting delays or errors in the assessment of casualties can lead to preventable deaths. We present a decision support framework that fuses outputs from multiple computer vision models, estimating signs of severe hemorrhage, respiratory distress, physical alertness, or visible trauma, into a Bayesian network constructed entirely from expert-defined rules. Unlike traditional data-driven models, our approach does not require training data, supports inference with incomplete information, and is robust to noisy or uncertain observations. This integration improves the reliability and consistency of casualty assessments and accelerates decision-making. When evaluated in two simulated but operationally realistic MCI scenarios involving 11 and 9 casualties, respectively, our Bayesian network model substantially outperformed vision-only baselines. The accuracy of physiological assessment improved from 15% to 42% in the first scenario and from 19% to 46% in the second, representing nearly three-fold performance gains. More importantly, overall triage accuracy increased from 14% to 53% in all patients, while the diagnostic coverage of the system expanded from 31% to 95% of the cases requiring assessment. These results demonstrate that expert-knowledge-guided probabilistic reasoning can significantly enhance automated triage systems, offering a promising approach to supporting emergency responders in mass casualty incidents where rapid and accurate patient prioritization is critical for their survival.