Prediction and influencing factors analysis of bronchopneumonia inpatients’ total hospitalization expenses based on BP neural network and support vector machine models_West China Medical Journal

Authors：

 KONG Xiaolan

Medical Affairs Department, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610072, P. R. China;

Corresponding author：

KONG Xiaolan, Email: 394731278@qq.com

Keywords：

BP neural network; Support vector machine; Two-step clustering method; Bronchopneumonia; Total hospitalization expenses; Prediction

DOI：

10.7507/1002-0179.201912096

Video：

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Objective To predict the total hospitalization expenses of bronchopneumonia inpatients in a tertiay hospital of Sichuan Province through BP neural network and support vector machine models, and analyze the influencing factors.Methods The home page information of 749 cases of bronchopneumonia discharged from a tertiay hospital of Sichuan Province in 2017 was collected and compiled. The BP neural network model and the support vector machine model were simulated by SPSS 20.0 and Clementine softwares respectively to predict the total hospitalization expenses and analyze the influencing factors.Results The accuracy rate of the BP neural network model in predicting the total hospitalization expenses was 81.2%, and the top three influencing factors and their importances were length of hospital stay (0.477), age (0.154), and discharge department (0.083). The accuracy rate of the support vector machine model in predicting the total hospitalization expenses was 93.4%, and the top three influencing factors and their importances were length of hospital stay (0.215), age (0.196), and marital status (0.172), but after stratified analysis by Mantel-Haenszel method, the correlation between marital status and total hospitalization expenses was not statistically significant (χ²=0.137, P=0.711).Conclusions The BP neural network model and the support vector machine model can be applied to predicting the total hospitalization expenses and analyzing the influencing factors of patients with bronchopneumonia. In this study, the prediction effect of the support vector machine is better than that of the BP neural network model. Length of hospital stay is an important influencing factor of total hospitalization expenses of bronchopneumonia patients, so shortening the length of hospital stay can significantly lighten the economic burden of these patients.

Citation： KONG Xiaolan. Prediction and influencing factors analysis of bronchopneumonia inpatients’ total hospitalization expenses based on BP neural network and support vector machine models. West China Medical Journal, 2021, 36(1): 55-60. doi: 10.7507/1002-0179.201912096 Copy

1.	绳慧峰, 刘晴, 许苹, 等. 人工神经网络在医疗风险预测中的应用研究. 中国卫生质量管理, 2017, 24(4): 15-17.
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10.	王娅玲, 张铎龄, 张译丹, 等. 多种统计模型在冠心病医疗费用影响因素中的应用与比较. 中国卫生事业管理, 2017, 34(3): 198-200, 204.
11.	饶飘雪, 叶枫. 基于Logistic回归、ANN、SVM的乳腺癌复发影响因素研究. 计算机系统应用, 2016, 25(7): 259-263.
12.	王文文, 周涛, 陆惠玲, 等. 基于聚类和支持向量机的胃癌患者住院费用建模研究. 中国初级卫生保健, 2016, 30(2): 1-4.
13.	沈培, 张吉凯, 张子刚. 基于支持向量机的单病种医疗费用控制研究. 中国卫生经济, 2012, 31(3): 89-91.
14.	王晶, 卫金茂, 由军平. 支持向量机及其在癌症诊断中的应用. 计算机工程与应用, 2005, 41(36): 220-222.
15.	成则丰, 程路遥, 金文英, 等. 基于FTIR-SVM的正常甲状腺及甲状腺癌组织的分类研究. 哈尔滨工程大学学报, 2006, 27(增刊 1): 366-369.

1. 绳慧峰, 刘晴, 许苹, 等. 人工神经网络在医疗风险预测中的应用研究. 中国卫生质量管理, 2017, 24(4): 15-17.
2. 魏晓静, 刘静. 基于BP神经网络的我国卫生总费用的预测研究. 中国卫生事业管理, 2014(3): 168-170.
3. 张旺锋, 高姝君, 要力, 等. 基于BP神经网络的山西省旅游人数预测. 甘肃科技, 2012, 28(22): 62-67.
4. 周峰, 郜艳晖, 贾卫东, 等. 基于神经网络聚类的慢性乙型肝炎住院患者治疗模式及费用. 中国卫生统计, 2018, 35(1): 7-10.
5. 黄薇, 康起明, 容芷君, 等. 基于人工神经网络的高血压预测模型. 广东医学, 2017, 38(15): 2315-2317, 2320.
6. 陈远方, 张熳, 王小莉, 等. ARIMA 模型和 BP 神经网络模型在我国乙型肝炎发病预测中的应用. 江苏预防医学, 2015, 26(3): 23-26.
7. 王静. 基于人工神经网络的旅游市场趋势预测方法研究. 北京: 经济科学出版社, 2012: 50.
8. 张颖, 李利杰, 刘海容, 等. 支持向量机模型在肺癌病人住院费用影响因素分析中的应用. 中国医院, 2014, 18(10): 30-32.
9. 张晔, 张晗, 尹玢璨, 等. 基于支持向量方法机构建急性胰腺炎患者住院天数预测模型. 医学信息学杂志, 2016, 37(2): 57-60, 65.
10. 王娅玲, 张铎龄, 张译丹, 等. 多种统计模型在冠心病医疗费用影响因素中的应用与比较. 中国卫生事业管理, 2017, 34(3): 198-200, 204.
11. 饶飘雪, 叶枫. 基于Logistic回归、ANN、SVM的乳腺癌复发影响因素研究. 计算机系统应用, 2016, 25(7): 259-263.
12. 王文文, 周涛, 陆惠玲, 等. 基于聚类和支持向量机的胃癌患者住院费用建模研究. 中国初级卫生保健, 2016, 30(2): 1-4.
13. 沈培, 张吉凯, 张子刚. 基于支持向量机的单病种医疗费用控制研究. 中国卫生经济, 2012, 31(3): 89-91.
14. 王晶, 卫金茂, 由军平. 支持向量机及其在癌症诊断中的应用. 计算机工程与应用, 2005, 41(36): 220-222.
15. 成则丰, 程路遥, 金文英, 等. 基于FTIR-SVM的正常甲状腺及甲状腺癌组织的分类研究. 哈尔滨工程大学学报, 2006, 27(增刊 1): 366-369.

West China Medical Journal

Prediction and influencing factors analysis of bronchopneumonia inpatients’ total hospitalization expenses based on BP neural network and support vector machine models

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