Development of an artificial intelligence model for triage in a military emergency department: Focusing on abdominal pain in soldiers

Background

In military settings, determining whether a patient with abdominal pain requires emergency care can be challenging due to the absence or inexperience of medical staff. Misjudging the severity of abdominal pain can lead to delayed treatment or unnecessary transfers, both of which consume valuable resources. Therefore, our aim was to develop an artificial intelligence model capable of classifying the urgency of abdominal pain cases, taking into account patient characteristics.

Methods

We collected structured and unstructured data from patients with abdominal pain visiting South Korean military hospital emergency rooms between January 2015 and 2020. After excluding patients with missing values, 20,432 patients were enrolled. Structured data consisted of age, sex, vital signs, past medical history, and symptoms, while unstructured data included preprocessed free text descriptions of chief complaints and present illness. Patients were divided into training, validation, and test datasets in an 8:1:1 ratio. Using structured data, we developed four conventional machine learning models and a novel mixed model, which combined one of the best performing machine learning models with emergency medical knowledge. And we also created a deep learning model using both structured and unstructured data.

Results

Xgboost demonstrated the highest performance among the six models, with an area under the precision-recall curve (AUPRC) score of 0.61. The other five models achieved AUPRC scores as follows: logistic regression (0.24), decision tree (0.22), multi-layer perceptron (0.21), deep neural network (0.58), and mixed model (0.58).

Conclusion

This study is the first to develop an AI model for identifying emergency cases of abdominal pain in a military setting. With more balanced and better-structured datasets, clinically significant AI model could be developed based on the findings of this study.