基于梯度增强回归树的COVID-19患者住院时间的预测模型
Zhihao Zhang1, Ting Zeng1,2, Yijia Wang3
1College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China.
Mathematical biosciences and engineering : MBE
|June 16, 2023
概括
预测COVID-19患者逗留时间 (LOS) 有助于医院资源管理. 梯度增强回归树模型使用临床数据准确预测LOS,改善疫情期间的医疗安排.
科学领域:
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 随着COVID-19的爆发,医疗资源受到压力,医院床位需求增加.
- 准确预测患者停留时间 (LOS) 对于有效的医院管理和资源分配至关重要.
研究的目的:
- 开发和验证COVID-19患者LOS的预测模型.
- 支持医院管理优化医疗资源的安排和利用.
主要方法:
- 对166名COVID-19患者进行了回顾性研究.
- 用渐变增强回归树 (GBRT) 来构建一个预测模型.
- 人口统计数据和临床指标被用作预测变量.
主要成果:
- 中位数LOS为17.0天,平均LOS为18.06天.
- 在GBRT模型中,平均平方误差 (MSE) 为23.84,平均绝对误差 (MAE) 为4.12,平均绝对百分比误差 (MAPE) 为0.76.
- LOS的主要预测因素包括肌酸激酶-MB (CK-MB),C反应蛋白 (CRP),肌酸激酶 (CK),白细胞计数 (WBC) 和患者年龄.
结论:
- GBRT模型准确地预测了COVID-19患者的LOS.
- 这种预测能力为公共卫生危机期间的医疗资源管理提供了宝贵的决策支持.
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