Walczak S, Scorpio R J
University of Colorado at Denver College of Business, Denver, CO (Dr. Walczak), and the Division of Pediatric Surgery, Floating Hospital for Children, Boston, MA (Dr. Scorpio).
Pediatr Crit Care Med. 2000 Jul;1(1):42-7. doi: 10.1097/00130478-200007000-00008.
To evaluate the efficacy of artificial neural networks in categorizing pediatric trauma patients into four distinct acuity of care groups and in determining the length of stay (LOS) within specific areas of the hospital. DESIGN: Using historical information from >8,000 pediatric trauma patient records, train and evaluate artificial neural networks to predict the injury severity and LOS for each patient in pediatric intensive care units (PICUs), step-down units, and floor units. Each artificial neural network is evaluated for categorization accuracy and mean absolute error difference on the predicted LOS. SUBJECTS: A total of 10,353 patient records from the National Pediatric Trauma registry, representing all pediatric trauma patients treated at affiliated hospitals from April 1994 through December 1996. Records with incomplete information were eliminated from the study, leaving 8,081 usable patient records. MEASUREMENTS: A total of 14 variables are selected from the 81 values present in the National Pediatric Trauma Registry as independent variables for the artificial neural networks. Each neural network produces nine output values: five for categorizing the patient's injury severity, three for the LOS in the PICU, step-down unit, and floor units, and one for the patient's total LOS. RESULTS: A fuzzy ARTMAP neural network accurately categorizes 88% of mortality patients and 58.3% of critical PICU patients. A backpropagation neural network succeeded in predicting the total LOS to within 1 day for 51.4% and the ICU LOS to within 1 day for 70.4% of all evaluated patients. CONCLUSION: Information available in the first 10 mins of a patient's presentation at the emergency room can be used by an artificial neural network to predict injury severity and LOS. Artificial neural networks enable more effective resource planning and patient management.
评估人工神经网络在将儿科创伤患者分类为四个不同护理 acuity 组以及确定患者在医院特定区域的住院时间（LOS）方面的功效。
利用来自 8000 多名儿科创伤患者记录的历史信息，训练并评估人工神经网络，以预测儿科重症监护病房（PICUs）、逐步降级病房和普通病房中每位患者的损伤严重程度和住院时间。对每个人工神经网络在预测住院时间方面的分类准确性和平均绝对误差差异进行评估。
来自国家儿科创伤登记处的总共 10353 份患者记录，代表 1994 年 4 月至 1996 年 12 月在附属医院接受治疗的所有儿科创伤患者。研究中排除了信息不完整的记录，留下 8081 份可用的患者记录。
从国家儿科创伤登记处存在的 81 个值中总共选择 14 个变量作为人工神经网络的自变量。每个神经网络产生九个输出值：五个用于对患者的损伤严重程度进行分类，三个用于在 PICUs、逐步降级病房和普通病房的住院时间，一个用于患者的总住院时间。
一个模糊 ARTMAP 神经网络准确地将 88%的死亡患者和 58.3%的重症 PICU 患者进行了分类。一个反向传播神经网络成功地将所有评估患者的总住院时间预测在 1 天以内的比例为 51.4%，将 ICU 住院时间预测在 1 天以内的比例为 70.4%。
人工神经网络可以利用患者在急诊室就诊的前 10 分钟内可用的信息来预测损伤严重程度和住院时间。人工神经网络能够实现更有效的资源规划和患者管理。
