Artificial intelligence to predict in-hospital mortality using novel anatomical injury score

Abstract

The aim of the study is to develop artificial intelligence (AI) algorithm based on a deep learning model to predict mortality using abbreviate injury score (AIS). The performance of the conventional anatomic injury severity score (ISS) system in predicting in-hospital mortality is still limited. AIS data of 42,933 patients registered in the Korean trauma data bank from four Korean regional trauma centers were enrolled. After excluding patients who were younger than 19 years old and those who died within six hours from arrival, we included 37,762 patients, of which 36,493 (96.6%) survived and 1269 (3.4%) deceased. To enhance the AI model performance, we reduced the AIS codes to 46 input values by organizing them according to the organ location (Region-46). The total AIS and six categories of the anatomic region in the ISS system (Region-6) were used to compare the input features. The AI models were compared with the conventional ISS and new ISS (NISS) systems. We evaluated the performance pertaining to the 12 combinations of the features and models. The highest accuracy (85.05%) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (83.62%), AIS with DNN (81.27%), ISS-16 (80.50%), NISS-16 (79.18%), NISS-25 (77.09%), and ISS-25 (70.82%). The highest AUROC (0.9084) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (0.9013), AIS with DNN (0.8819), ISS (0.8709), and NISS (0.8681). The proposed deep learning scheme with feature combination exhibited high accuracy metrics such as the balanced accuracy and AUROC than the conventional ISS and NISS systems. We expect that our trial would be a cornerstone of more complex combination model.

Figure 1

Overview of training and validation of the AI models to predict the in-hospital mortality in trauma patients: (a) Process flow of the AI model development, and (b) DNN (Region-46) architecture with a five-layer deep neural network consisting of an input layer, three fully connected (FC) layers, and output layer.

Figure 2

Receiver operating characteristic curves: (a) ISS, NISS, DNN (AIS), DNN (Region-6) and DNN (Region-46), and (b) four models of logistic regression (LR), random forest (RF), support vector machine (SVM) and DNN based on Region-46: LR (Region-46), RF (Region-46), SVM (Region-46) and DNN (Region-46), respectively.