Nayara Cristina da Silva, M. Albertini, A. R. Backes, G. Pena
{"title":"Prediction of readmissions in hospitalized children and adolescents by machine learning","authors":"Nayara Cristina da Silva, M. Albertini, A. R. Backes, G. Pena","doi":"10.1145/3555776.3577592","DOIUrl":null,"url":null,"abstract":"Pediatric hospital readmission involves greater burdens for the patient and their family network, and for the health system. Machine learning can be a good strategy to expand knowledge in this area and to assist in the identification of patients at readmission risk. The objective of the study was to develop a predictive model to identify children and adolescents at high risk of potentially avoidable 30-day readmission using a machine learning approach. Retrospective cohort study with patients under 18 years old admitted to a tertiary university hospital. We collected demographic, clinical, and nutritional data from electronic databases. We apply machine learning techniques to build the predictive models. The 30-day hospital readmissions rate was 9.50%. The accuracy for CART model with bagging was 0.79, the sensitivity, and specificity were 76.30% and 64.40%, respectively. Machine learning approaches can predict avoidable 30-day pediatric hospital readmission into tertiary assistance.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3555776.3577592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Pediatric hospital readmission involves greater burdens for the patient and their family network, and for the health system. Machine learning can be a good strategy to expand knowledge in this area and to assist in the identification of patients at readmission risk. The objective of the study was to develop a predictive model to identify children and adolescents at high risk of potentially avoidable 30-day readmission using a machine learning approach. Retrospective cohort study with patients under 18 years old admitted to a tertiary university hospital. We collected demographic, clinical, and nutritional data from electronic databases. We apply machine learning techniques to build the predictive models. The 30-day hospital readmissions rate was 9.50%. The accuracy for CART model with bagging was 0.79, the sensitivity, and specificity were 76.30% and 64.40%, respectively. Machine learning approaches can predict avoidable 30-day pediatric hospital readmission into tertiary assistance.