基于ATERDE和专家经验的混合DGC智能导诊算法  

Hybrid DGC intelligent triage algorithm based on ATERDE and expert experience

作　　者：旷珊珊 白梅娟 郭赵斌 路巍[1] 霍振宇[1] 侯帅[1] Kuang Shanshan;Bai Meijuan;Guo Zhaobin;Lu Wei;Huo Zhenyu;Hou Shuai(School of Information and Electrical Engineering,Hebei University of Engineering,Handan,Hebei 056038,China;Wu'an First People's Hospital of Pediatrics)

机构地区：[1]河北工程大学信息与电气工程学院,河北邯郸056038 [2]武安市第一人民医院儿科

出　　处：《计算机时代》2022年第3期36-40,共5页Computer Era

基　　金：国家重点研发计划课题(No.2020YFB1709903);河北省科技计划项目(21350101D);邯郸市科学技术研究与发展计划项目(19422101008-27)。

摘　　要：针对医院人工导诊效率和精确度较低的问题,提出了一种基于ATERDE(Automatic threshold Elites Regeneration Differential Evolution)和专家经验的混合DGC(Data Gravitation Classify)智能导诊算法。采用一种基于自动阈值的ERDE算法(ATERDE),以选出最优的客观权重矩阵;将ATERDE算法与专家经验融合,构建包含主客观信息的属性重要度权重矩阵;最后提出一种兼顾全局引力与局部引力的DGC算法,以减少数据不平衡对分类结果的影响。实验结果表明,该方法平均分类精度达到87%以上,精确度有明显的提升。Aiming at the problem of low efficiency and precision of hospital manual triage,a hybrid Data Gravity Classification(DGC)intelligent triage algorithm based on Automatic Threshold Elites Regeneration Differential Evolution(ATERDE)and expert experience is proposed.ATERDE algorithm is used to select the optimal objective weight matrix;an attribute importance weight matrix containing subjective and objective information is constructed by integrating ATERDE algorithm with expert experience;finally,a DGC algorithm considering both global gravity and local gravity is proposed to reduce the influence of data imbalance on the classification results.The experimental results show that the average classification accuracy of this algorithm reaches more than 87%,it is significantly improved.

关 键 词：混合权重 专家经验 数据引力 智能导诊 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]