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| Intelligent diagnosis of jaundice with dynamic uncertain causality graph model |
| Shao-rui Hao, Shi-chao Geng, Lin-xiao Fan, Jia-jia Chen, Qin Zhang, Lan-juan Li |
| State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China; School of Communication, Shandong Normal University, Jinan 250014, China; School of Computer Science and Engineering, Beihang University, Beijing 100191, China |
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Abstract Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.
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Received: 15 June 2016
Published: 04 May 2017
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基于动态不确定性因果图（DUCG）模型的黄疸待查智能诊断研究
目的：黄疸待查是一个常见而复杂的临床问题，涉及到内、外、妇、儿等多个学科。目前我国医学专家存在数量相对不足，分布不均匀等情况，导致了区域性和部门性医疗服务水平不足。本研究旨在建立一个客观的黄疸待查智能诊断系统，以提高医学诊断的正确性，提升基层医院及急诊的诊断水平，同时减少病人的花费。
创新点：本研究采用了国际先进的动态不确定性因果图（DUCG）模型，建立了黄疸待查相关疾病的知识库，通过203例临床病例的测试，其准确率达99.01%。文章以图形化的方式给出了疾病的诊断过程，方便医师理解和学习。
方法：本研究采用了DUCG模型进行疾病诊断，首先根据DUCG模型的定义和黄疸诊断思路建立了包含27种黄疸相关疾病（表4）的知识库（图2），其中包括了疾病的危险因素、临床症状和体征、客观检查检验结果等。然后与根据DUCG算法（公式1-4）编写的推理软件相结合形成诊断系统，对203例临床黄疸患者进行智能诊断，准确率达99.01%。最后对一例丙型病毒性肝炎患者的具体诊断过程进行了拆解阐述，体现了DUCG模型适用于复杂逻辑关系、计算效率高、不依赖推理概率和结果易于理解等优点。
结论：DUCG模型成功实现了对黄疸待查相关疾病的智能诊断，准确率高，实用性好。该方法具有在其他医学领域推广应用的价值。
关键词：
动态不确定性因果图（DUCG）,
人工智能,
智能诊断
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