An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

Abstract

Background Within the trauma system, the emergency department (ED) is the hospital’s first contact and is vital for allocating medical resources. However, there is generally limited information about patients that die in the ED. Objective The aim of this study was to develop an artificial intelligence (AI) model to predict trauma mortality and analyze pertinent mortality factors for all patients visiting the ED. Methods We used the Korean National Emergency Department Information System (NEDIS) data set (N=6,536,306), incorporating over 400 hospitals between 2016 and 2019. We included the International Classification of Disease 10th Revision (ICD-10) codes and chose the following input features to predict ED patient mortality: age, sex, intentionality, injury, emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and vital signs. We compared three different feature set performances for AI input: all features (n=921), ICD-10 features (n=878), and features excluding ICD-10 codes (n=43). We devised various machine learning models with an ensemble approach via 5-fold cross-validation and compared the performance of each model with that of traditional prediction models. Lastly, we investigated explainable AI feature effects and deployed our final AI model on a public website, providing access to our mortality prediction results among patients visiting the ED. Results Our proposed AI model with the all-feature set achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.9974 (adaptive boosting [AdaBoost], AdaBoost + light gradient boosting machine [LightGBM]: Ensemble), outperforming other state-of-the-art machine learning and traditional prediction models, including extreme gradient boosting (AUROC=0.9972), LightGBM (AUROC=0.9973), ICD-based injury severity scores (AUC=0.9328 for the inclusive model and AUROC=0.9567 for the exclusive model), and KTAS (AUROC=0.9405). In addition, our proposed AI model outperformed a cutting-edge AI model designed for in-hospital mortality prediction (AUROC=0.7675) for all ED visitors. From the AI model, we also discovered that age and unresponsiveness (coma) were the top two mortality predictors among patients visiting the ED, followed by oxygen saturation, multiple rib fractures (ICD-10 code S224), painful response (stupor, semicoma), and lumbar vertebra fracture (ICD-10 code S320). Conclusions Our proposed AI model exhibits remarkable accuracy in predicting ED mortality. Including the necessity for external validation, a large nationwide data set would provide a more accurate model and minimize overfitting. We anticipate that our AI-based risk calculator tool will substantially aid health care providers, particularly regarding triage and early diagnosis for trauma patients.

Drinking from the Holy Grail—Does a Perfect Triage System Exist? And Where to Look for It?

“…Computational models can also predict the risk of death during hospitalization better than triage systems alone [ 47 , 50 ]. According to various researchers, good discriminatory results for sudden cardiac arrest (SCA) in the ED, obtained solely from triage data, were obtained using Random Forest (AUC 0.931) [ 53 ], Logistic Regression (AUC 0.925) [ 15 ], and Ada Boosting and Light Gradient Boosting machine (AUC 0.997) [ 58 ].…”

“…Summarizing the comparison between computational models and traditional triage systems, the authors state that artificial intelligence is more effective in predicting the main final points (hospitalization, admission to ICU, and death) [ 15 , 50 , 51 , 52 , 53 , 57 , 58 ]. Moreover, they can improve the quality of care in the ED and reduce the burden on healthcare systems [ 32 ].…”

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)

“…Many papers reporting the use of ML models in medicine have used a large clinical data set to make diagnostic or prognostic predictions [3][4][5][6]. However, the use of data from electronic health records and other resources is often not without pitfalls as these data are typically collected and optimized for other purposes (eg, medical billing) [7].…”