[1]宋 波,辛文贤,冯云霞.基于 BP 神经网络的临床路径优化[J].计算机技术与发展,2020,30(04):156-160.[doi:10. 3969 / j. issn. 1673-629X. 2020. 04. 030]
　SONG Bo,XIN Wen-xian,FENG Yun-xia.Clinical Path Optimization Based on BP Neural Network[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(04):156-160.[doi:10. 3969 / j. issn. 1673-629X. 2020. 04. 030]
点击复制

基于 BP 神经网络的临床路径优化()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年04期
页码:
156-160
栏目:
应用开发研究
出版日期:
2020-04-10

文章信息/Info

Title:
Clinical Path Optimization Based on BP Neural Network
文章编号:
1673-629X(2020)04-0156-05
作者:
宋 波; 辛文贤; 冯云霞
青岛科技大学 信息科学技术学院,山东 青岛 266061
Author(s):
SONG Bo; XIN Wen-xian; FENG Yun-xia
School of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China
关键词:
BP 神经网络; 临床路径; 治疗天数; 诊疗单元; 优化
Keywords:
BP neural network; clinical pathway; treatment days; treatment unit; optimization
分类号:
TP399
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 04. 030
摘要:
针对临床路径的复杂性和模糊不确定性,对其进行综合分析,计算出临床路径诊疗单元的平均治疗天数以及临床路径的总治疗天数,分别作为实验的样本数据和最终评价指标。 在此基础上,利用 BP 神经网络的方法对临床路径进行优化建模。 并通过实验对比分析隐层神经元数量对临床路径优化结果的影响,发现神经元数量增多时,错误率明显下降,迭代次数呈上下波动。 最后选取 7-9-1 的 BP 神经网络结构,以某地区三甲医院的糖尿病加高血压临床路径为例,进行仿真实验。结果显示,训练模型的输出值与期望值之间的相对误差范围在 0～0.2% 之内,测试模型的输出值与期望值之间的相对误差范围在 0～0.1% 之内。 说明该模型具有较强的自学习自适应能力,能够有效地优化临床路径,减少患者的治疗天数。
Abstract:
In view of the complexity and fuzzy uncertainty of the clinical pathway,a comprehensive analysis is carried out to calculate the average treatment days of the treatment unit and the total treatment days of the clinical pathway,respectively as the experimental sample data and the final evaluation indicators. On this basis,the BP neural network is used to optimize and model the clinical pathway. Theeffect of the number of hidden layer neurons on the optimization of the clinical pathway is compared and analyzed by experiment. It was found that when neurons increasing,the error rate decreases significantly and the number of iteration fluctuates up and down. Finally,the simulation experiment is performed by selecting the BP neural network structure of 7-9-1,with the clinical pathway of diabetes and hypertension in the top three hospitals in a certain area as an example. The results show that the relative error range between the output value and the expected value of the training model is within 0～0.2% ,while that of the test model is within 0～0.1% . It indicates that the model has strong self-learning and self-adaptive,and can effectively optimize the clinical pathway and reduce the treatment days of patients.

相似文献/References:

[1]浦佳祺,陈德旺.基于最小二乘法和BP神经网络的TOA 定位算法[J].计算机技术与发展,2018,28(05):5.[doi:10.3969/j.issn.1673-629X.2018.05.002]
　PU Jia-qi,CHEN De-wang.A TOA Positioning Algorithm Based on Least Square Method and BP Neural Network[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(04):5.[doi:10.3969/j.issn.1673-629X.2018.05.002]
[2]王嵘冰,徐红艳,李 波,等.BP 神经网络隐含层节点数确定方法研究[J].计算机技术与发展,2018,28(04):31.[doi:10.3969/ j. issn.1673-629X.2018.04.007]
　WANG Rong-bing,XU Hong-yan,LI Bo,et al.Research on Method of Determining Hidden Layer Nodes in BP Neural Network[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(04):31.[doi:10.3969/ j. issn.1673-629X.2018.04.007]
[3]李春生,李霄野,张可佳.基于遗传算法改进的 BP 神经网络房价预测分析[J].计算机技术与发展,2018,28(08):144.[doi:10.3969/ j. issn.1673-629X.2018.08.030]
　LI Chun-sheng,LI Xiao-ye,ZHANG Ke-jia.Price Forecasting Analysis of BP Neural Network Based on Improved Genetic Algorithm[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(04):144.[doi:10.3969/ j. issn.1673-629X.2018.08.030]
[4]辛月振,孙贝贝,夏盛瑜.数据挖掘方法在生物实验数据上的应用[J].计算机技术与发展,2018,28(09):143.[doi:10.3969/ j. issn.1673-629X.2018.09.029]
　XIN Yue-zhen,SUN Bei-bei,XIA Sheng-yu.Application of Data Mining Method in Biological Experiment Data[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2018,28(04):143.[doi:10.3969/ j. issn.1673-629X.2018.09.029]
[5]丁红卫,王文果,万 良,等.基于 BP 神经网络的电网物资需求预测研究[J].计算机技术与发展,2019,29(06):138.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 029]
　DING Hong-wei,WANG Wen-guo,WAN Liang,et al.Research on Material Demand Prediction of Power Network Based on BP Neural Network[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2019,29(04):138.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 029]
[6]姬晓飞,石宇辰.多分类器融合的光学遥感图像目标识别算法[J].计算机技术与发展,2019,29(11):52.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 011]
　JI Xiao-fei,SHI Yu-chen.Optical Remote Sensing Image Object Recognition Based on Multiple Classifications Fusion[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2019,29(04):52.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 011]
[7]邓晓政,叶 冰.免疫 BP 网络的机载嵌入式训练系统效能评估[J].计算机技术与发展,2019,29(12):173.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 031]
　DENG Xiao-zheng,YE Bing.Effectiveness Evaluation of Airborne Embedded Training System of Immune BP Networks[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2019,29(04):173.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 031]
[8]李佩钰.一种基于小波和神经网络的短时交通流量预测[J].计算机技术与发展,2020,30(01):135.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 024]
　LI Pei-yu.Short-term Traffic Flow Prediction Based on Wavelet and Neural Network[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(04):135.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 024]
[9]余肖生,宋 锦,任明霞,等.基于伪度量的案例推理改进算法[J].计算机技术与发展,2020,30(10):69.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 013]
　YU Xiao-sheng,SONG Jin,REN Ming-xia,et al.Improved Case Reasoning Algorithm Based on Pseudo-Metric[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(04):69.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 013]
[10]曾 坤,姜志侠*.基于多遗传算法的 BP 神经网络人脸识别[J].计算机技术与发展,2021,31(01):77.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 014]
　ZENG Kun,JIANG Zhi-xia*.Face Recognition Based on BP Neural Network with Multiple Genetic Algorithm[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2021,31(04):77.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 014]

更新日期/Last Update: 2020-04-10