Tu J V, Guerriere M R
Information Systems Department, St. Michael's Hospital, University of Toronto, Canada.
Comput Biomed Res. 1993 Jun;26(3):220-9. doi: 10.1006/cbmr.1993.1015.
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. 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, 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 trained network could potentially be used as a predictive instrument for optimizing the scheduling of cardiac surgery patients in times of limited ICU resources. Neural networks are a new 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。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（定义为住院超过2天）的患者。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的三个风险组（低、中、高），对应的延长住院频率分别为16.3%、35.3%和60.8%。在ICU资源有限的情况下，经过训练的网络有可能用作优化心脏手术患者排期的预测工具。神经网络是开发预测工具的一种新方法，与其他更广泛使用的统计技术相比，它有优点也有缺点。
