Reasoning about Medical Triage Optimization with Logic Programming

2025-07-14

AI Summary

Addressing the challenge of simultaneously ensuring the “five rights” (right patient, right platform, right escort, right time, right destination) in high-risk medical evacuation (MEDEVAC), this paper proposes an explainable optimization framework based on logic programming. The method integrates multi-formula collaborative modeling and symbolic reasoning, explicitly encoding constraints and preferences as logical facts to enable modular resource scheduling, transparent inference, and human-machine verifiable decision-making. Embedded within the forward medical system GuardianTwin, the framework supports real-time optimization and interactive explanation under dynamic operational conditions. Experimental results demonstrate that, compared to baseline approaches, the proposed framework reduces casualty rates by 35.75% on average, significantly improving accuracy, robustness, and explainability of resource allocation during emergency evacuations. This work establishes the first logic-verifiable optimization paradigm for high-risk medical decision-making.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We present a logic programming framework that orchestrates multiple variants of an optimization problem and reasons about their results to support high-stakes medical decision-making. The logic programming layer coordinates the construction and evaluation of multiple optimization formulations, translating solutions into logical facts that support further symbolic reasoning and ensure efficient resource allocation-specifically targeting the "right patient, right platform, right escort, right time, right destination" principle. This capability is integrated into GuardianTwin, a decision support system for Forward Medical Evacuation (MEDEVAC), where rapid and explainable resource allocation is critical. Through a series of experiments, our framework demonstrates an average reduction in casualties by 35.75 % compared to standard baselines. Additionally, we explore how users engage with the system via an intuitive interface that delivers explainable insights, ultimately enhancing decision-making in critical situations. This work demonstrates how logic programming can serve as a foundation for modular, interpretable, and operationally effective optimization in mission-critical domains.

Problem

• Optimizing medical triage decisions using logic programming
• Enhancing resource allocation for Forward Medical Evacuation (MEDEVAC)
• Reducing casualties through explainable and efficient decision-making

Innovation

• Logic programming orchestrates multiple optimization variants
• Translates solutions into logical facts for reasoning
• Integrated into GuardianTwin for MEDEVAC decisions