利用机器学习预测住院儿童和青少年的再入院情况

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Nayara Cristina da Silva, M. Albertini, A. R. Backes, G. Pena
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引用次数: 0

摘要

儿科医院再入院给患者及其家庭网络以及卫生系统带来了更大的负担。机器学习可以是一个很好的策略来扩展这一领域的知识,并帮助识别有再入院风险的患者。该研究的目的是开发一种预测模型,以识别使用机器学习方法可能避免30天再入院的高风险儿童和青少年。回顾性队列研究在18岁以下的患者入院的第三大学医院。我们从电子数据库中收集了人口统计、临床和营养数据。我们运用机器学习技术来建立预测模型。30天再入院率为9.50%。带套袋的CART模型准确率为0.79,敏感性为76.30%,特异性为64.40%。机器学习方法可以预测可避免的30天儿科医院三级辅助再入院情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of readmissions in hospitalized children and adolescents by machine learning
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.
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
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40.00%
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8
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