Frize M, Wang L, Ennett C M, Nickerson B G, Solven F G, Stevenson M
University of Ottawa, S.I.T.E., ON.
Proc AMIA Symp. 1998:553-7.
An earlier version (2.0) of the case-based reasoning (CBR) tool, called IDEAS for ICU's, allowed users to compare the ten closest matching cases to the newest patient admission, using a large database of intensive care patient records, and physician-selected matching-weights [1,2]. The new version incorporates matching-weights, which have been determined quantitatively. A faster CBR matching engine has also been incorporated into the new CBR. In a second approach, a back-propagation, feed-forward artificial neural network estimated two classes of the outcome "duration of artificial ventilation" for a subset of the database used for the CBR work. Weight-elimination was successfully applied to reduce the number of input variables and speed-up the estimation of outcomes. New experiments examined the impact of using a different number of input variables on the performance of the ANN, measured by correct classification rates (CCR) and the Average Squared Error (ASE).
基于案例推理（CBR）工具的早期版本（2.0）名为“ICU的IDEAS”，它允许用户使用大量重症监护患者记录数据库和医生选择的匹配权重，将与最新入院患者最匹配的十个案例进行比较[1,2]。新版本纳入了已通过定量确定的匹配权重。新的CBR还采用了速度更快的CBR匹配引擎。在第二种方法中，一个反向传播、前馈人工神经网络针对用于CBR工作的数据库子集估计了“人工通气持续时间”结果的两类情况。成功应用了权重消除以减少输入变量的数量并加快结果估计。新的实验研究了使用不同数量的输入变量对人工神经网络性能的影响，通过正确分类率（CCR）和平均平方误差（ASE）来衡量。
