Tang Jun, Li Yang, Luo Keyu, Lai Jiangyuan, Yin Xiang, Wu Dongdong
Department of Information, Daping Hospital, Army Medical University, No.10 Daping Changjiang Branch Road, Yuzhong District, Chongqing, 400042, China, 86 18302302369.
Department of Emergency Medicine, Medical Center of Trauma and War Injury, Daping Hospital, Army Medical University, Chongqing, 400042, China.
JMIR Form Res. 2025 May 29;9:e67311. doi: 10.2196/67311.
Deaths related to physical trauma impose a heavy burden on society, and the Abbreviated Injury Scale (AIS) is an important tool for injury research. AIS covers injuries to various parts of the human body and scores them based on the severity of the injury. In practical applications, the complex AIS coding rules require experts to encode by consulting patient medical records, which inevitably increases the difficulty, time, and cost of evaluation of patient and also puts higher demands on the workload of information collection and processing. In some cases, the sheer number of patients or the inability to access detailed medical records necessary for coding further complicates independent AIS codes.
This study aims to use advanced deep learning techniques to predict AIS codes based on easily accessible diagnostic information of patients to improve the accuracy of trauma assessment.
We used a dataset of patients with trauma (n=26,810) collected by the Chongqing Daping Hospital between October 2013 and June 2024. We mainly selected the patient's diagnostic information, injury description, cause of injury, injury region, injury types, and present illness history as the key feature inputs. We used a robust optimization Bidirectional Encoder Representations from Transformers (BERT) pretraining method to embed these features and constructed a prediction model based on BERT. This model aims to predict AIS codes and comprehensively evaluate its performance through a 5-fold cross-validation. We compared the BERT model with previous research results and current mainstream machine learning methods to verify its advantages in prediction tasks. In addition, we also conducted external validation of the model using 244 external data points from the Chongqing Emergency Center.
The BERT model proposed in this paper performs significantly better than the comparison model on independent test datasets with an accuracy of 0.8971, which surpassed the previous study by 10 % points. In addition, the area under the curve (AUC value of the BERT model is 0.9970, and the F1-score is 0.8434. In the external dataset, the accuracy, AUC, and F1-score results of the model are 0.7131, 0.8586, and 0.6801, respectively. These results indicate that our model has high generalization ability and prediction accuracy.
The BERT model we proposed is mainly based on diagnostic information to predict AIS codes, and its prediction accuracy is superior to previous investigations and current mainstream machine learning methods. It has a high generalization ability in external datasets.
与身体创伤相关的死亡给社会带来了沉重负担，简明损伤定级（AIS）是损伤研究的重要工具。AIS涵盖人体各个部位的损伤，并根据损伤的严重程度对其进行评分。在实际应用中，复杂的AIS编码规则需要专家通过查阅患者病历进行编码，这不可避免地增加了患者评估的难度、时间和成本，也对信息收集和处理的工作量提出了更高要求。在某些情况下，患者数量众多或无法获取编码所需的详细病历，使得独立的AIS编码更加复杂。
本研究旨在使用先进的深度学习技术，基于患者易于获取的诊断信息预测AIS编码，以提高创伤评估的准确性。
我们使用了重庆大坪医院在2013年10月至2024年6月期间收集的创伤患者数据集（n = 26,810）。我们主要选择患者的诊断信息、损伤描述、损伤原因、损伤部位、损伤类型和现病史作为关键特征输入。我们使用一种稳健优化的基于变换器的双向编码器表征（BERT）预训练方法来嵌入这些特征，并构建了一个基于BERT的预测模型。该模型旨在预测AIS编码，并通过五折交叉验证全面评估其性能。我们将BERT模型与先前的研究结果和当前主流的机器学习方法进行比较，以验证其在预测任务中的优势。此外，我们还使用重庆急救中心的244个外部数据点对该模型进行了外部验证。
本文提出的BERT模型在独立测试数据集上的表现明显优于比较模型，准确率为0.8971，比先前的研究高出10个百分点。此外，BERT模型的曲线下面积（AUC值）为0.9970，F1分数为0.8434。在外部数据集中，该模型的准确率、AUC和F1分数结果分别为0.7131、0.8586和0.6801。这些结果表明我们的模型具有较高的泛化能力和预测准确性。
我们提出的BERT模型主要基于诊断信息预测AIS编码，其预测准确性优于先前的研究和当前主流的机器学习方法。它在外部数据集中具有较高的泛化能力。
