不同训练策略对用于预测住院的贝叶斯网络准确性的影响
Impact of different training strategies on the accuracy of a Bayesian network for predicting hospital admission.
作者信息
Leegon Jeffrey, Aronsky Dominik
机构信息
School of Informatics, University of Edinburgh, Edinburgh..
出版信息
AMIA Annu Symp Proc. 2006;2006:474-8.
INTRODUCTION
The healthcare environment is constantly changing. Probabilistic clinical decision support systems need to recognize and incorporate the changing patterns and adjust the decision model to maintain high levels of accuracy.
METHODS
Using data from >75,000 ED patients during a 19-month study period we examined the impact of various static and dynamic training strategies on a decision support system designed to predict hospital admission status for ED patients. Training durations ranged from 1 to 12 weeks. During the study period major institutional changes occurred that affected the system's performance level.
RESULTS
The average area under the receiver operating characteristic curve was higher and more stable when longer training periods were used. The system showed higher accuracy when retrained an updated with more recent data as compared to static training period.
DISCUSSION
To adjust for temporal trends the accuracy of decision support systems can benefit from longer training periods and retraining with more recent data.
引言
医疗环境在不断变化。概率性临床决策支持系统需要识别并纳入不断变化的模式，调整决策模型以保持较高的准确性。
方法
在为期19个月的研究期间，我们使用了超过75000名急诊科患者的数据，研究了各种静态和动态训练策略对一个旨在预测急诊科患者住院状态的决策支持系统的影响。训练时长从1周至12周不等。在研究期间，发生了重大的机构变革，影响了系统的性能水平。
结果
使用较长训练期时，受试者操作特征曲线下的平均面积更高且更稳定。与静态训练期相比，当使用更新的近期数据重新训练时，该系统显示出更高的准确性。
讨论
为了适应时间趋势，决策支持系统的准确性可受益于更长的训练期以及使用更新的近期数据进行再训练。
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