基于深度神经网络的平均住院日影响因素分析  

Analysis of influencing factors on average length of hospital stay based on deep neural networks

作　　者：秦欣云 罗丹 叶晨 QIN Xinyun;LUO Dan;YE Chen(The Affiliated Hospital of Guizhou Medical University,Guiyang 550000,China;Guizhou University,Guiyang 550000,China)

机构地区：[1]贵州医科大学附属医院,贵州贵阳550000 [2]贵州大学,贵州贵阳550000

出　　处：《现代医院》2025年第3期388-392,共5页Modern Hospitals

基　　金：贵州省卫生健康委科学技术基金项目(gzwkj2022-275)。

摘　　要：目的通过深度神经网络(deep neural networks,DNN)分析影响平均住院日的主要因素,剔除冗余因素,提高平均住院日的管理成效。方法基于机器学习中主成分分析法对平均住院日的多因素特征进行降维,提取主要因素。然后利用深度神经网络学习主要因素之间的权重关系,对真实的平均住院日进行预测。研究采用的数据来自某三甲医院HIS系统中2021年131740条住院患者病案首页。结果2021年该院平均住院日的主要影响因素是术前平均住院日、输血反应、入院年份、年龄、入院途径、医疗付费方式,对应的权重绝对值依次是2.58、1.89、1.77、0.96、0.76、0.75;t检验对预测平均住院日与实际平均住院日进行比较分析,结果显示两者不具备显著性差异(P>0.05)。结论基于主要因素的DNN模型能有效地预测真实平均住院日,针对平均住院日主要影响因素分类施策可以有效提高平均住院日的管理效率。Objective By analyzing the main factors affecting the average length of stay through deep neural networks,redundant factors are eliminated to improve the management effectiveness of the average length of stay.Methods Based on principal component analysis in machine learning,the multi factor features of average length of hospital stay are reduced and the main factors are extracted.Then,deep neural networks are used to learn the weight relationships between the main factors and predict the actual average length of hospital stay.The data used in this article comes from the homepage of 131740 inpatient medical records in the HIS system of a tertiary hospital in 2021.Results The main influencing factors of the average length of stay in the hospital in 2021 are preoperative average length of stay,transfusion reactions,admission year,age,admission route,and medical payment method.The corresponding absolute weight values are 2.58,1.89,1.77,0.96,0.76,and 0.75,respectively;The t-test compared and analyzed the predicted average length of stay with the actual average length of stay,and the results showed that there was no significant difference between the two(P>0.05).Conclusion The DNN model based on the main factors can effectively predict the actual average length of stay,and the hospital’s classification of the main influencing factors of the average length of stay obtained in this article can effectively improve the management efficiency of the average length of stay.

关 键 词：深度神经网络 平均住院日 影响因素 权重 效果分析 

分 类 号：R197.3[医药卫生—卫生事业管理]