基于循环神经网络的医疗人力资源配置模型设计与仿真  

Design and simulation of medical human resource allocation model based on cyclic neural network

作　　者：刘欣[1] 岳晓磊 靖超[1] 王亚林[1] LIU Xin;YUE Xiaolei;JING Chao;WANG Yalin(The First Affiliated Hospital of Hebei North University,Zhangjiakou 075000,China)

机构地区：[1]河北北方学院附属第一医院,河北张家口075000

出　　处：《电子设计工程》2021年第11期39-43,共5页Electronic Design Engineering

基　　金：2020年度河北省“三三三人才工程”资助项目(A202001018)。

摘　　要：人力资源合理化管理对医院高效培养医疗领域人才、完善医院管理模式以及提高医院整体资源利用率均有较大的帮助。当前所应用于人力资源领域的计算模型通常是基于统计算法展开的,已无法满足海量数据的处理需求,未考虑到数据隐藏的特征,这容易导致信息匮乏问题的出现。文中将循环卷积神经网络和传统人力资源配置算法相结合,设计了适用于人力资源领域的人岗匹配推荐算法,可以改善传统算法数据训练质量问题。在算法实验部分,文中算法F1值为0.823,相较其他两项算法分别提升了20.1%和7.4%,说明该算法可以提升数据的隐层特征,进而提升数据的训练质量,提高人岗匹配度和推荐精准度。The rational management of human resources is of great help to cultivate talents in the medical field,improve the hospital management mode and improve the overall utilization rate of hospital resources.At present,the calculation models used in the field of human resources are often based on statistical algorithm,which can not meet the processing needs of massive data,and do not consider the characteristics of data hiding,which easily leads to the problem of information shortage.In this paper,the circular convolution neural network and the traditional human resource allocation algorithm are combined to design a person post matching recommendation algorithm suitable for the field of human resources,which can improve the data training quality of the traditional algorithm.In the experimental part of the algorithm,the F1 value of this algorithm is 0.823,which is 20.1%and 7.4%higher than the other two algorithms,which shows that the algorithm can improve the hidden layer characteristics of data,and then improve the training quality of data and the recommendation accuracy of person post matching degree.

关 键 词：人力资源配置 循环神经网络 数据挖掘 人岗匹配度 深度学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]