使用贝叶斯模型的患者分类概率模型

P. Tansitpong
{"title":"使用贝叶斯模型的患者分类概率模型","authors":"P. Tansitpong","doi":"10.4018/ijrqeh.348579","DOIUrl":null,"url":null,"abstract":"The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on staffing, resource allocation, and operational strategy. The variation in probability estimates across different observations improves the predictive usefulness, hence strengthening the effectiveness in healthcare management and planning.","PeriodicalId":36298,"journal":{"name":"International Journal of Reliable and Quality E-Healthcare","volume":" 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Probabilistic Model of Patient Classification Using Bayesian Model\",\"authors\":\"P. Tansitpong\",\"doi\":\"10.4018/ijrqeh.348579\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on staffing, resource allocation, and operational strategy. The variation in probability estimates across different observations improves the predictive usefulness, hence strengthening the effectiveness in healthcare management and planning.\",\"PeriodicalId\":36298,\"journal\":{\"name\":\"International Journal of Reliable and Quality E-Healthcare\",\"volume\":\" 3\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Reliable and Quality E-Healthcare\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijrqeh.348579\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Nursing\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Reliable and Quality E-Healthcare","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijrqeh.348579","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Nursing","Score":null,"Total":0}
引用次数: 0

摘要

该研究强调贝叶斯分类算法在预测医疗机构病人就诊情况方面的有效性。贝叶斯算法检查过去的病人数据,检测入院动态中的复杂模式,包括人口、临床和时间因素。通过使用贝叶斯原理,预测模型能够估算出某些患者人口统计学特征在某些时间间隔内出现的概率,从而协助资源分配和运营管理。估算出的概率可用于选择人员配备、资源分配和运营策略。不同观察结果中概率估计值的差异提高了预测的有用性,从而加强了医疗管理和规划的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Probabilistic Model of Patient Classification Using Bayesian Model
The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on staffing, resource allocation, and operational strategy. The variation in probability estimates across different observations improves the predictive usefulness, hence strengthening the effectiveness in healthcare management and planning.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.20
自引率
0.00%
发文量
43
期刊最新文献
Probabilistic Model of Patient Classification Using Bayesian Model A New Classification Model Based on Transfer Learning of DCNN and Stacknet for Fast Classification of Pneumonia Through X-Ray Images The Effect of E-Learning and Traditional Teaching Done Hand-in-Hand for First-Year M.B.B.S. Students Decentralized Blockchain-Enabled Employee Authentication System Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1