用专家规则构建贝叶斯网络，提升自动伤员分诊的准确率与覆盖度。
Multimodal Bayesian Network for Robust Assessment of Casualties in Autonomous Triage
- 融合多视觉模型输出，基于专家规则构建贝叶斯网络进行推理
- 伤情评估准确率从15%→42%、整体分诊准确率达53%，覆盖95%需评估病例
- 无需训练数据，支持不完整或噪声数据下的鲁棒推断，适合应急场景
大规模伤亡事件会压垮医疗系统，伤员评估延误或错误可能导致可避免死亡。我们提出一个决策支持框架，融合多个计算机视觉模型对严重出血、呼吸窘迫、意识状态及可见创伤的检测结果，构建完全由专家定义规则组成的贝叶斯网络。与传统数据驱动模型不同，本方法无需训练数据，支持不完整信息推理，且对噪声和不确定性观测具有鲁棒性。在DARPA分诊挑战赛（DTC）实地场景中，针对11名和9名伤员的两场任务评估显示，该贝叶斯网络模型显著优于仅依赖视觉的基线方法：生理评估准确率分别从15%提升至42%，从19%提升至46%；整体分诊准确率从14%升至53%，系统诊断覆盖范围从31%扩展至95%。这些结果表明，基于专家知识的概率推理能显著增强自动化分诊系统，为应急响应提供有力支持。该方法助力团队Chiron在DTC第一轮实体赛中取得第4名。
原文摘要 · Abstract (English)
Mass Casualty Incidents 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. We report performance for two missions involving 11 and 9 casualties, respectively, where our Bayesian network model substantially outperformed vision-only baselines during evaluation of our system in the DARPA Triage Challenge (DTC) field scenarios. The accuracy of physiological assessment improved from 15% to 42% in the first scenario and from 19% to 46% in the second, representing nearly threefold increase in performance. 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 MCIs. This approach enabled Team Chiron to achieve 4th place out of 11 teams during the 1st physical round of the DTC.
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