Wen Yuxin, Rahman Md Fashiar, Zhuang Yan, Pokojovy Michael, Xu Honglun, McCaffrey Peter, Vo Alexander, Walser Eric, Moen Scott, Tseng Tzu-Liang Bill
Dale E. and Sarah Ann Fowler School of Engineering, Chapman University, Orange, CA 92866, USA.
Department of Industrial, Manufacturing and Systems Engineering, The University of Texas at El Paso, El Paso, TX 79968, USA.
Mach Learn Appl. 2022 Sep 15;9:100365. doi: 10.1016/j.mlwa.2022.100365. Epub 2022 Jun 18.
Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six proposed survival models is evaluated and compared based on clinical data from COVID-19 patients.
在整个疫情期间，在维持最佳资源利用的同时提供及时的患者护理是医院一直面临的核心运营挑战之一。住院时间（LOS）是医院效率、患者护理质量和运营恢复力的重要指标。众多研究人员已开发出回归或分类模型来预测住院时间。然而，传统模型缺乏利用典型的删失临床数据的能力。我们建议使用事件发生时间建模技术（也称为生存分析），根据从多个来源收集的个性化信息来预测患者的住院时间。基于COVID-19患者的临床数据，对所提出的六个生存模型的性能进行了评估和比较。
