{"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}
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.