标题： 动态不确定因果图在临床诊断中的方法论和现实应用，具有可解释性和不变性 显示英文标题

标题： Methodology and Real-World Applications of Dynamic Uncertain Causality Graph for Clinical Diagnosis with Explainability and Invariance

摘要： 人工智能辅助的临床诊断在医疗护理中是被期望的。 现有的深度学习模型缺乏可解释性，主要专注于图像分析。 最近开发的动态不确定性因果图（DUCG）方法是基于因果关系的，具有可解释性，并且在不同的应用场景中具有不变性，不会出现数据收集、标记、拟合、隐私、偏差、泛化、高成本和高能耗等问题。 通过临床专家和DUCG技术人员的紧密合作，构建了涵盖54个主要症状的46个DUCG模型。 超过1000种疾病可以在不进行分诊的情况下进行诊断。 在实际应用之前，这46个DUCG模型由第三方医院进行了回顾性验证。 验证的诊断精度不低于95%，其中每种疾病的诊断精度，包括罕见病，都不低于80%。 经过验证后，这46个DUCG模型在中国的实际场景中得到了应用。 已经进行了超过一百万例真实的诊断案例，仅发现了17例错误诊断。 由于DUCG的透明性，导致错误诊断的错误被发现并得到了纠正。 经常应用DUCG的临床医生的诊断能力得到了显著提高。 在介绍先前提出的DUCG方法之后，提出了潜在医疗检查的推荐算法，并提取了DUCG的核心思想。 显示英文摘要

摘要： AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, and invariant across different application scenarios, without problems of data collection, labeling, fitting, privacy, bias, generalization, high cost and high energy consumption. Through close collaboration between clinical experts and DUCG technicians, 46 DUCG models covering 54 chief complaints were constructed. Over 1,000 diseases can be diagnosed without triage. Before being applied in real-world, the 46 DUCG models were retrospectively verified by third-party hospitals. The verified diagnostic precisions were no less than 95%, in which the diagnostic precision for every disease including uncommon ones was no less than 80%. After verifications, the 46 DUCG models were applied in the real-world in China. Over one million real diagnosis cases have been performed, with only 17 incorrect diagnoses identified. Due to DUCG's transparency, the mistakes causing the incorrect diagnoses were found and corrected. The diagnostic abilities of the clinicians who applied DUCG frequently were improved significantly. Following the introduction to the earlier presented DUCG methodology, the recommendation algorithm for potential medical checks is presented and the key idea of DUCG is extracted.