预测重症监护病房死亡率：平稳和非平稳时间模型的比较
Predicting ICU mortality: a comparison of stationary and nonstationary temporal models.
作者信息
Kayaalp M, Cooper G F, Clermont G
机构信息
Center for Biomedical Informatics, Intelligent Systems Program, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
出版信息
Proc AMIA Symp. 2000:418-22.
OBJECTIVE
This study evaluates the effectiveness of the stationarity assumption in predicting the mortality of intensive care unit (ICU) patients at the ICU discharge.
DESIGN
This is a comparative study. A stationary temporal Bayesian network learned from data was compared to a set of (33) nonstationary temporal Bayesian networks learned from data. A process observed as a sequence of events is stationary if its stochastic properties stay the same when the sequence is shifted in a positive or negative direction by a constant time parameter. The temporal Bayesian networks forecast mortalities of patients, where each patient has one record per day. The predictive performance of the stationary model is compared with nonstationary models using the area under the receiver operating characteristics (ROC) curves.
RESULTS
The stationary model usually performed best. However, one nonstationary model using large data sets performed significantly better than the stationary model.
CONCLUSION
Results suggest that using a combination of stationary and nonstationary models may predict better than using either alone.
目的
本研究评估平稳性假设在预测重症监护病房（ICU）患者出院时死亡率方面的有效性。
设计
这是一项比较研究。将从数据中学习得到的一个平稳时间贝叶斯网络与从数据中学习得到的一组（33个）非平稳时间贝叶斯网络进行比较。如果一个作为事件序列观察到的过程在序列以恒定时间参数正向或负向移动时其随机特性保持不变，那么该过程就是平稳的。时间贝叶斯网络预测患者的死亡率，其中每位患者每天有一条记录。使用接收器操作特征（ROC）曲线下的面积将平稳模型的预测性能与非平稳模型进行比较。
结果
平稳模型通常表现最佳。然而，一个使用大数据集的非平稳模型的表现明显优于平稳模型。
结论
结果表明，结合使用平稳模型和非平稳模型可能比单独使用任何一种模型的预测效果更好。
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