Tu J V, Guerriere M R
Information Systems Department, St. Michael's Hospital, University of Toronto, Ontario.
Proc Annu Symp Comput Appl Med Care. 1992:666-72.
A patient's intensive care unit (ICU) length of stay following cardiac surgery is an important issue in Canada, where cardiovascular intensive care resources are limited and waiting lists for cardiac surgery exist. A predictive instrument for ICU length of stay could lead to improved utilization of existing ICU and operating room resources through better scheduling of patients and staff. We trained a neural network with a database of 713 patients and 15 input variables to predict patients who would have a prolonged ICU length of stay, which we defined as a stay greater than 2 days. In an independent test set of 696 patients, the network was able to stratify patients into three risk groups for prolonged stay (low, intermediate, and high), corresponding to frequencies of prolonged stay of 16.3%, 35.3%, and 60.8% respectively. The performance of the network was also evaluated by calculating the area under the Receiver Operating Characteristic (ROC) curve in the training set, 0.7094 (SE 0.0224), and in the test set, 0.6960 (SE 0.0227). We believe the trained network would be a useful predictive instrument for optimizing the scheduling of cardiac surgery patients in times of limited ICU resources. Neural networks are a new alternative method for developing predictive instruments that offer both advantages and disadvantages when compared to other more widely used statistical techniques.
在加拿大，心脏手术后患者在重症监护病房（ICU）的住院时长是一个重要问题，因为该国心血管重症监护资源有限，且存在心脏手术等候名单。一种用于预测ICU住院时长的工具，能够通过更合理地安排患者和医护人员，提高现有ICU和手术室资源的利用效率。我们使用一个包含713名患者和15个输入变量的数据库训练了一个神经网络，以预测那些ICU住院时长会延长的患者，我们将延长的住院时长定义为超过2天。在一个由696名患者组成的独立测试集中，该网络能够将患者分为延长住院时长的三个风险组（低、中、高），对应的延长住院时长频率分别为16.3%、35.3%和60.8%。通过计算训练集和测试集中接收者操作特征（ROC）曲线下的面积，分别为0.7094（标准误0.0224）和0.6960（标准误0.0227），对该网络的性能进行了评估。我们认为，在ICU资源有限的情况下，经过训练的网络将是优化心脏手术患者安排的一种有用的预测工具。神经网络是开发预测工具的一种新的替代方法，与其他更广泛使用的统计技术相比，它既有优点也有缺点。
