Analysis of influencing factors on average length of hospital stay based on deep neural networks
10.3969/j.issn.1671-332X.2025.03.016
- VernacularTitle:基于深度神经网络的平均住院日影响因素分析
- Author:
Xinyun QIN
1
;
Dan LUO
;
Chen YE
Author Information
1. 贵州医科大学附属医院 贵州贵阳 550000
- Publication Type:Journal Article
- Keywords:
Deep neural network;
Average length of stay;
Influence factors;
Weight;
Effect analysis
- From:
Modern Hospital
2025;25(3):388-392
- CountryChina
- Language:Chinese
-
Abstract:
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 prin-cipal 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 131 740 inpatient medical re-cords 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 ef-fectively 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.
