[1]杨皓然,姚瑶.基于Petri网的医疗流程建模优化[J].延边大学学报(自然科学版),2018,44(04):332-335.
　YANG Haoran,YAO Yao.Medical process modeling optimization based on Petri Net[J].Journal of Yanbian University,2018,44(04):332-335.
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基于Petri网的医疗流程建模优化

《延边大学学报(自然科学版)》[ISSN:1004-4353/CN:22-1191/N] 卷: 第44卷 期数: 2018年04期 页码: 332-335 栏目: 应用科学研究 出版日期: 2018-12-30
Title:
Medical process modeling optimization based on Petri Net
作者:
杨皓然1;  姚瑶2
( 1.安徽理工大学 数学与大数据学院; 2.安徽理工大学 经济与管理学院: 安徽 淮南 232001 )
Author(s):
YANG Haoran1;  YAO Yao2
( 1.School of Mathematics and Big Data, Anhui University of Science and Technology; 2.School of Economics and Management, Anhui University of Science and Technology: Huainan 232001, China )
关键词:
Petri网;  医疗流程模型;  优化
Keywords:
Petri Net;  medical process model;  optimization
分类号:
TP391.9
文献标志码:
A
摘要:
为减少患者就医时抽血化验的次数,提出了一种基于Petri网的医疗流程模型的优化分析方法.首先介绍了Petri网的基本概念; 然后在确保合理的医疗流程前提下,通过合并变迁对医疗流程进行了建模优化; 最后利用PIPE软件对优化后的模型进行了模拟运行,结果表明本文方法能够有效改善医疗流程,节省医疗资源.
Abstract:
In order to reduce the number of blood samples taken from patients, an optimized analysis method of medical process model based on Petri Net was proposed. Firstly, the basic concepts of Petri Net are introduced, and then modeling and optimizing medical processes through mergers transitions while ensuring reasonable medical processes. Finally, simulate the optimized model with PIPE software. The results show that this method can effectively improve medical procedures and save medical resources.

参考文献/References:


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[5] Fang Xianwen, Wu Junzhi, Liu Xiangwei. An optimized method of business process mining based on the behavior profile of Petri Nets[J]. Information Technology Journal, 2014,13(1):86-93.
[6] Wang Shouguang, Zhou Mengzhou, Li Zhiwu, et al. A new modified reachability tree approach and its applications to unbounded Petri Nets[J]. IEEE Transactions on Systems Man & Cybernetics Systems, 2013,43(4):932-940.
[7] 李雪萍.基于Petri网行为轮廓的医疗业务流程挖掘方法[D].淮南:安徽理工大学,2017.
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备注/Memo

收稿日期: 2018-08-19 作者简介: 杨皓然(1995—),男,硕士研究生,研究方向为Petri网.
基金项目: 国家自然科学基金资助项目(61402011,61572035); 安徽省自然科学基金资助项目(1508085MF111, 1608085QF149); 安徽省高校自然科学基金资助重点项目(KJ2016A208)

更新日期/Last Update: 2018-12-30