Mining Syndrome Differentiating Principles from Traditional Chinese Medicine Clinical Data

Syndrome differentiation-based treatment is one of the key characteristics of Traditional Chinese Medicine(TCM).The process of syndrome differentiation is difficult and challenging due to its complexity,diversity and vagueness.Analyzing syndrome principles from historical records of TCM using data mining(DM)technology has been of high interest in recent years.Nevertheless,in most relevant studies,existing DM algorithms have been simply developed for TCM mining,while the combination of TCM theories or its characteristics with DM algorithms has rarely been reported.This paper presents a novel Symptom-Syndrome Topic Model(SSTM),which is a supervised probabilistic topic model with three-tier Bayesian structure.In the SSTM,syndromes are considered as observed topic labels to distinguish certain symptoms from possible symptoms according to their different positions.The generation of our model is in full compliance with the syndrome differentiation theory of TCM.Experimental results show that the SSTM is more effective than other models for syndrome differentiating.

基于遗传算法的综合布线路径布局研究

以综合布线系统的路径规划为研究对象,对布线路径中的公共路径和最短路径的双目标进行统筹规划,以满足不同情况下的不同施工需求.首先给出了综合布线决策的整数规划模型,采用遗传算法构建了一种新的综合布线优化算法,用于进行综合布线路径规划的设计与研究.在遗传算法的基础上,通过加权组合的方式实现公共路径和最短路径的协调,其中公共路径越长越节约工期,最短路径越长越节约成本.最后,通过仿真对所提模型和方法的有效性进行了验证.