Resembling the role of disease diagnosis in Western medicine, pathogenesis (also called Bing Ji) diagnosis is one of the utmost important tasks in traditional Chinese medicine (TCM). In TCM theory, pathogenesis is a complex system composed of a group of interrelated factors, which is highly consistent with the character of systems science (SS). In this paper, we introduce a heuristic definition called pathogenesis network (PN) to represent pathogenesis in the form of the directed graph. Accordingly, a computational method of pathogenesis diagnosis, called network differentiation (ND), is proposed by integrating the holism principle in SS. ND consists of three stages. The first stage is to generate all possible diagnoses by Cartesian Product operated on specified prior knowledge corresponding to the input symptoms. The second stage is to screen the validated diagnoses by holism principle. The third stage is to pick out the clinical diagnosis by physician-computer interaction. Some theorems are stated and proved for the further optimization of ND in this paper. We conducted simulation experiments on 100 clinical cases. The experimental results show that our proposed method has an excellent capability to fit the holistic thinking in the process of physician inference.

类似西医诊病作用，病机（又称病机）) 诊断是中医 (TCM) 中最重要的任务之一。在中医理论中，病机是由一组相互关联的因素组成的复杂系统，与系统科学的特征高度一致。在本文中，我们引入了一种称为发病机制网络（PN）的启发式定义，以有向图的形式表示发病机制。因此，通过在 SS 中整合整体原理，提出了一种称为网络分化 (ND) 的发病机制诊断计算方法。ND由三个阶段组成。第一阶段是通过笛卡尔积生成所有可能的诊断，该乘积对与输入症状相对应的特定先验知识进行操作。第二阶段是通过整体原则筛选已验证的诊断。第三阶段是通过医机交互挑选出临床诊断。本文陈述并证明了一些定理，用于进一步优化ND。我们对100个临床病例进行了模拟实验。实验结果表明，我们提出的方法具有很好的适应医生推理过程中整体思维的能力。