贝叶斯模型的平均值用于预测与COVID-19住院时间长度相关的因素
Shabnam Bahrami1, Karimollah Hajian-Tilaki2,3, Masomeh Bayani4
1Student Research Center, Research Institute, Babol University of Medical Sciences, Babol, Iran.
BMC medical research methodology
|July 6, 2023
概括
贝叶斯模型平均 (BMA) 确定了COVID-19住院时间 (LOHS) 的关键预测因素. 接受ICU治疗,呼吸困难和糖尿病显著影响LOHS,为临床管理和资源分配提供信息.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 对于COVID-19患者来说,住院时间长 (LOHS) 带来了巨大的财务和心理负担.
- 确定LOHS的预测因素对于医疗保健系统管理和患者护理至关重要.
研究的目的:
- 确定COVID-19的关键预测因素住院时间 (LOHS).
- 评估贝叶斯模型平均 (BMA) 与LOHS预测的经典和机器学习模型的性能.
主要方法:
- 一项对4996名COVID-19患者的历史队列研究.
- 六种模型的比较:逐步,AIC,BIC (经典线性回归),两种BMA方法 (奥卡姆窗口,MCMC) 和梯度增强决策树 (GBDT).
- 对人口统计,临床和生物标志物数据的分析,以预测LOHS.
主要成果:
- 平均LOHS为6.7 ± 5.7天.
- 使用奥卡姆窗口的贝叶斯模型平均 (BMA) 与经典模型相比显示出更高的性能 (R2 = 0.174).
- 显著的LOHS预测因素包括ICU入院,呼吸困难,年龄,糖尿病,CRP,PO2,WBC,AST,BUN和NLR.
结论:
- 使用Occam's Window方法的BMA为识别影响COVID-19 LOHS的因素提供了更好的匹配和预测性能.
- 研究结果突出了预测COVID-19患者长时间住院治疗的关键临床指标.
关键词:
在AICIC AICIC中,您可以使用AICIC.在BIC BIC中,我们可以看到.贝叶斯模型的平均值是贝叶斯的模型.在 COVID-19 疫情中,在GBDTGBDTGBDTGBDTGBDTGBDTGBD在医院住院的时间.马尔科夫链蒙特卡洛 (MCMC) 是一个奥卡姆的窗户 奥卡姆的窗户一步一步地一步一步地.更多相关视频
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