机器学习用于儿科人口医院再入院预测
Nayara Cristina da Silva1, Marcelo Keese Albertini2, André Ricardo Backes3
1Graduate Program in Health Sciences, Federal University of Uberlandia, Uberlandia, Minas Gerais, Brazil, Pará Av, 1720, Campus Umuarama, Uberlândia, Minas Gerais 38400-902, Brazil.
Computer methods and programs in biomedicine
|December 22, 2023
概括
机器学习模型,特别是XGBoost,可以有效地预测潜在的可避免的30天儿科医院再入院. 这项技术有助于早期识别有风险的儿童,使得有针对性的干预措施和减少医疗保健负担.
科学领域:
- 使用先进的机器学习 (ML) 技术用于医疗保健中的预测建模.
- 专注于在第三级护理环境中对儿科再接收风险评估.
背景情况:
- 儿科再接收对患者,家庭和医疗保健系统造成重大负担.
- 准确识别高风险患者对于有效的资源分配和干预至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测儿科患者潜在可避免的30天再入院情况.
- 确定与再接收风险增加相关的关键临床和人口因素.
主要方法:
- 对9080名儿科患者的回顾性队列研究,这些患者被录入第三级大学医院.
- 六个ML算法 (CART,RF,GBM,XGBoost,决策树,LR) 应用于一个训练/测试数据集 (75%/25%).
- 用AUC,灵敏度,特异性和Youden的J指数来评估模型的性能.
主要成果:
- 可避免的30天再入院率为9.5%.
- XGBoost,随机森林,GBM和CART显示了可比的性能 (AUC). XGBoost,随机森林,GBM和CART显示了可比的性能.
- 使用包装归算的XGBoost获得了最高的Youden's J-index (0.484) 值,AUC为0.814.
- 关键预测因素包括癌症诊断,年龄,红细胞计数,白细胞,红细胞分布宽度,水平,选择性入院和多病症.
结论:
- 机器学习,特别是XGBoost,显示出预测30天儿科再入院的强大潜力.
- 在医院系统中实施可以促进早期风险识别和有针对性的干预措施.
- 该模型有助于优化医疗保健策略,以预防儿科再入院.
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