Using Traditional and Deep Machine Learning to Predict Emergency Room Triage Levels

Abstract

Accurate triage in emergency rooms is crucial for efficient patient care and resource allocation. We developed methods to predict triage levels using several traditional machine learning methods (logistic regression, random forest, XGBoost) and neural network deep learning-based approaches. These models were tested on a dataset from emergency department visits of patients at a local Turkish hospital; this dataset consists of both structured and unstructured data. Compared with previous work, our challenge was to build a predictive model that uses documents written in the Turkish language and that handles specific aspects of the Turkish medical system. Text embedding techniques such as Bag of Words, Word2Vec, and BERT-based embedding were used to process the unstructured patient complaints. We used a comprehensive set of features including patient history data and disease diagnosis within our predictive models, which included advanced neural network architectures such as convolutional neural networks, attention mechanisms, and long-short-term memory networks. Our results revealed that BERT embeddings significantly enhanced the performance of neural network models, while Word2Vec embeddings showed slight better results in traditional machine learning models. The most effective model was XGBoost combined with Word2Vec embeddings, achieving 86.7% AUC, 81.5% accuracy, and 68.7% weighted F1 score. We conclude that text embedding methods and machine learning methods are effective tools to predict emergency room triage levels. The integration of patient history into the models, alongside the strategic use of text embeddings, significantly improves predictive accuracy.