Prediction of readmissions in hospitalized children and adolescents by machine learning

被引：0

作者：

da Silva, Nayara Cristina ^{[1
]}

Albertini, Marcelo Keese ^{[2
]}

Backes, Andre Ricardo ^{[3
]}

Pena, Georgia das Gracas ^{[1
]}

机构：

[1] Univ Fed Uberlandia, Grad Program Hlth Sci, Uberlandia, MG, Brazil

[2] Univ Fed Uberlandia, Sch Comp Sci, Uberlandia, MG, Brazil

[3] Univ Fed Sao Carlos, Dept Comp, Sao Carlos, SP, Brazil

来源：

38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023 | 2023年

关键词：

Machine learning; data analysis; hospital readmission; children health; adolescents health;

D O I：

10.1145/3555776.3577592

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：1088 / 1091

页数：4