Modelling Length of Stay in Hospitals using Multinomial Regression

S. Harini, M. Subbiah, M. R. Srinivasan
{"title":"Modelling Length of Stay in Hospitals using Multinomial Regression","authors":"S. Harini, M. Subbiah, M. R. Srinivasan","doi":"10.12785/IJCTS/060202","DOIUrl":null,"url":null,"abstract":"Hospital management is generally focused on studying the length of stay of patients since the measure has an impact on hospital resources. It is a challenging task for the hospital management to model the length of stay as they are asymmetric and heterogeneous in nature. Diabetes is a major health problem prevalent worldwide which leads to hospitalization over a time period. The present study deals with stay of diabetes patients classified as very short, short, medium and long duration of stay based on quantile classification rather than arbitrary approach. In this study, we have attempted to include an important covariate known as medical record since it assist in reducing the stay of a patient and can thereby accommodate more patients deserving treatment as inpatients. Based on the multiple levels of the response variable, we have considered fitting multinomial regression model for length of stay on diabetes. Further, this study has considered the validation of variable selection procedure for model fitting using subsampling approach. In conclusion, it has been identified that medical records is one of the important factor affecting the stay of patients and subsampling approach has been helpful in building the final model.","PeriodicalId":130559,"journal":{"name":"International Journal of Computational & Theoretical Statistics","volume":"230 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computational & Theoretical Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12785/IJCTS/060202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Hospital management is generally focused on studying the length of stay of patients since the measure has an impact on hospital resources. It is a challenging task for the hospital management to model the length of stay as they are asymmetric and heterogeneous in nature. Diabetes is a major health problem prevalent worldwide which leads to hospitalization over a time period. The present study deals with stay of diabetes patients classified as very short, short, medium and long duration of stay based on quantile classification rather than arbitrary approach. In this study, we have attempted to include an important covariate known as medical record since it assist in reducing the stay of a patient and can thereby accommodate more patients deserving treatment as inpatients. Based on the multiple levels of the response variable, we have considered fitting multinomial regression model for length of stay on diabetes. Further, this study has considered the validation of variable selection procedure for model fitting using subsampling approach. In conclusion, it has been identified that medical records is one of the important factor affecting the stay of patients and subsampling approach has been helpful in building the final model.
利用多项回归对住院时间进行建模
医院管理通常关注于研究患者的住院时间,因为这一措施会影响医院的资源。对于医院管理来说,建立住院时间模型是一项具有挑战性的任务,因为它们本质上是不对称和异构的。糖尿病是世界范围内普遍存在的主要健康问题,在一段时间内导致住院治疗。本研究采用分位数法对糖尿病患者的住院时间分为极短、短、中、长,而不是任意的方法。在本研究中,我们试图纳入一个重要的协变量,即医疗记录,因为它有助于减少患者的住院时间,从而可以容纳更多值得住院治疗的患者。基于反应变量的多重水平,我们考虑拟合糖尿病住院时间的多项回归模型。此外,本研究还考虑了使用子抽样方法进行模型拟合的变量选择过程的验证。综上所述,病历是影响患者住院的重要因素之一,亚抽样方法有助于构建最终模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信