停留时间预测与急性和紧急护理的标准化医院数据使用深度神经网络
Vincent Lequertier1,2, Tao Wang3, Julien Fondrevelle2
1Research on Healthcare Performance RESHAPE, Inserm U1290, Université Claude Bernard Lyon 1, Lyon, France.
Medical care
|February 12, 2024
概括
使用Feed-Forward神经网络 (FFNN) 的深度学习模型显著改善了急性护理中的停留时间 (LOS) 预测. 这种先进的方法比传统的医疗管理模型提供了更好的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 预测分析是一种预测分析.
背景情况:
- 住院时间 (LOS) 是医院运营和患者护理安排的关键指标.
- 准确的LOS预测对于资源分配和急性护理机构的质量改善至关重要.
研究的目的:
- 开发和评估一种基于深度学习的方法,用于使用行政数据预测停留时间 (LOS).
- 将拟议的深度学习模型与传统机器学习方法的性能进行比较.
主要方法:
- 一个Feed-Forward神经网络 (FFNN) 模型是使用来自1,140,100次住院的行政数据开发的.
- 该FFNN模型利用患者人口统计学,诊断,程序和时间信息进行细粒度的LOS预测.
- 通过5倍交叉验证,对随机森林和后勤回归使用准确度,科恩卡帕和布兰德-阿尔特曼图表进行了性能评估.
主要成果:
- 该FFNN模型实现了卓越的性能,精度为0.944和kappa为0.943.
- 随机森林和物流回归显示精度明显较低 (分别为0.574和0.352) 和卡帕斯 (分别为0.602和0.414).
- 与随机森林 (-6.72至6.83) 和后勤回归 (-7.60至9.20) 相比,FFNN显示了更窄的协议边界 (-2.73至2.67).
结论:
- 在预测停留时间方面,Feed-Forward神经网络 (FFNN) 模型显著超过随机森林和物流回归.
- 在常规急性护理环境中实施FFNN模型有可能提高患者护理质量和运营效率.
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