{"title":"基于神经网络的组合预测法的医院管理实践","authors":"Qi Yang","doi":"10.4018/ijhisi.342091","DOIUrl":null,"url":null,"abstract":"In this article, the outpatient volume, hospitalization income and drug demand in hospital management are taken as the research objects, and a neural network combined prediction model is established to predict the outpatient volume with the fitting prediction results of cubic polynomial regression model and grey model as the input of the network and the actual statistical outpatient volume as the output. Lasso variable selection method is used to determine the main indexes affecting the income of inpatients in hospital, and a prediction model combining grey prediction and artificial neural network is established to predict the income of inpatients in hospital. By studying the key characteristics of hospital drug demand, BP neural network, RBF neural network and GRNN generalized regression neural network are selected to predict the drug demand. By solving the quadratic programming problem and according to the weight rules, a combination forecasting model based on neural network is established to predict the drug demand, and the accuracy and stability of the model are evaluated.","PeriodicalId":101861,"journal":{"name":"Int. J. Heal. Inf. Syst. Informatics","volume":"52 2","pages":"1-13"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hospital Management Practice of Combined Prediction Method Based on Neural Network\",\"authors\":\"Qi Yang\",\"doi\":\"10.4018/ijhisi.342091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, the outpatient volume, hospitalization income and drug demand in hospital management are taken as the research objects, and a neural network combined prediction model is established to predict the outpatient volume with the fitting prediction results of cubic polynomial regression model and grey model as the input of the network and the actual statistical outpatient volume as the output. Lasso variable selection method is used to determine the main indexes affecting the income of inpatients in hospital, and a prediction model combining grey prediction and artificial neural network is established to predict the income of inpatients in hospital. By studying the key characteristics of hospital drug demand, BP neural network, RBF neural network and GRNN generalized regression neural network are selected to predict the drug demand. By solving the quadratic programming problem and according to the weight rules, a combination forecasting model based on neural network is established to predict the drug demand, and the accuracy and stability of the model are evaluated.\",\"PeriodicalId\":101861,\"journal\":{\"name\":\"Int. J. Heal. Inf. Syst. Informatics\",\"volume\":\"52 2\",\"pages\":\"1-13\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Heal. Inf. Syst. Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijhisi.342091\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Heal. Inf. Syst. Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijhisi.342091","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本文以医院管理中的门诊量、住院收入和药品需求为研究对象,以三次多项式回归模型和灰色模型的拟合预测结果为网络输入,以实际统计的门诊量为输出,建立了神经网络组合预测模型对门诊量进行预测。采用拉索变量选择法确定影响住院患者收入的主要指标,建立灰色预测与人工神经网络相结合的预测模型,预测住院患者收入。通过研究医院药品需求的主要特征,选择 BP 神经网络、RBF 神经网络和 GRNN 广义回归神经网络对药品需求进行预测。通过求解二次编程问题并根据权重规则,建立了基于神经网络的组合预测模型来预测药品需求,并对模型的准确性和稳定性进行了评估。
Hospital Management Practice of Combined Prediction Method Based on Neural Network
In this article, the outpatient volume, hospitalization income and drug demand in hospital management are taken as the research objects, and a neural network combined prediction model is established to predict the outpatient volume with the fitting prediction results of cubic polynomial regression model and grey model as the input of the network and the actual statistical outpatient volume as the output. Lasso variable selection method is used to determine the main indexes affecting the income of inpatients in hospital, and a prediction model combining grey prediction and artificial neural network is established to predict the income of inpatients in hospital. By studying the key characteristics of hospital drug demand, BP neural network, RBF neural network and GRNN generalized regression neural network are selected to predict the drug demand. By solving the quadratic programming problem and according to the weight rules, a combination forecasting model based on neural network is established to predict the drug demand, and the accuracy and stability of the model are evaluated.