人工神经网络可以预测创伤量和严重程度，而不受中心规模和地理位置的影响：一项多中心研究。
Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study.
机构信息
From the Division of Trauma and Surgical Critical Care, (B.M.D., O.D.G.), Vanderbilt University Medical Center, Nashville, Tennessee; Department of Surgery (D.P.S.), The Johns Hopkins Hospital, Baltimore, Maryland; Department of Surgery (R.A.C.), University of California San Francisco, San Francisco, California; Department of General Surgery, Iowa Methodist Medical Center (R.A.S.), Des Moines, Iowa; Division of Acute Care Surgery, Department of Surgery, University of Rochester Medical Center (N.A.S.), Rochester, New York; Department of Surgery, Denver Health Medical Center (M.J.C.), Denver, Colorado; and Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery, Memorial Hermann Hospital/Texas Medical Center (B.A.C.), Houston, Texas.
出版信息
J Trauma Acute Care Surg. 2019 Jul;87(1):181-187. doi: 10.1097/TA.0000000000002320.
BACKGROUND
Trauma has long been considered unpredictable. Artificial neural networks (ANN) have recently shown the ability to predict admission volume, acuity, and operative needs at a single trauma center with very high reliability. This model has not been tested in a multicenter model with differing climate and geography. We hypothesize that an ANN can accurately predict trauma admission volume, penetrating trauma admissions, and mean Injury Severity Score (ISS) with a high degree of reliability across multiple trauma centers.
METHODS
Three years of admission data were collected from five geographically distinct US Level I trauma centers. Patients with incomplete data, pediatric patients, and primary thermal injuries were excluded. Daily number of traumas, number of penetrating cases, and mean ISS were tabulated from each center along with National Oceanic and Atmospheric Administration data from local airports. We trained a single two-layer feed-forward ANN on a random majority (70%) partitioning of data from all centers using Bayesian Regularization and minimizing mean squared error. Pearson's product-moment correlation coefficient was calculated for each partition, each trauma center, and for high- and low-volume days (>1 standard deviation above or below mean total number of traumas).
RESULTS
There were 5,410 days included. There were 43,380 traumas, including 4,982 penetrating traumas. The mean ISS was 11.78 (SD = 6.12). On the training partition, we achieved R = 0.8733. On the testing partition (new data to the model), we achieved R = 0.8732, with a combined R = 0.8732. For high- and low-volume days, we achieved R = 0.8934 and R = 0.7963, respectively.
CONCLUSION
An ANN successfully predicted trauma volumes and acuity across multiple trauma centers with very high levels of reliability. The correlation was highest during periods of peak volume. This can potentially provide a framework for determining resource allocation at both the trauma system level and the individual hospital level.
LEVEL OF EVIDENCE
Care Management, level IV.
背景
创伤一直被认为是不可预测的。人工神经网络（ANN）最近已经显示出在单个创伤中心以非常高的可靠性预测入院量、严重程度和手术需求的能力。该模型尚未在具有不同气候和地理位置的多中心模型中进行测试。我们假设 ANN 可以准确预测多个创伤中心的创伤入院量、穿透性创伤入院量和平均损伤严重程度评分（ISS），具有高度可靠性。
方法
从五个地理位置不同的美国一级创伤中心收集了三年的入院数据。排除数据不完整、儿科患者和原发性热损伤患者。从每个中心以及当地机场的国家海洋和大气管理局数据中计算每日创伤次数、穿透性病例数和平均 ISS。我们使用贝叶斯正则化和最小化均方误差在所有中心数据的随机多数（70%）分区上训练单个两层前馈 ANN。对于每个分区、每个创伤中心以及高和低容量日（高于或低于总创伤次数平均值的 1 个标准差），计算 Pearson 积矩相关系数。
结果
共有 5410 天的数据。有 43380 例创伤，包括 4982 例穿透性创伤。平均 ISS 为 11.78（SD=6.12）。在训练分区中，我们达到了 R=0.8733。在测试分区（模型的新数据）中，我们达到了 R=0.8732，合并 R=0.8732。对于高和低容量日，我们分别达到了 R=0.8934 和 R=0.7963。
结论
ANN 成功地预测了多个创伤中心的创伤量和严重程度，具有非常高的可靠性。在高峰期，相关性最高。这可能为在创伤系统和医院个体层面上确定资源分配提供了一个框架。
证据水平
护理管理，四级。
Zotero 插件