Comparative Analysis of a Large Language Model and Machine Learning Method for Prediction of Hospitalization from Nurse Triage Notes: Implications for Machine Learning-based Resource Management

Predicting hospitalization from nurse triage notes has significant implications in health informatics. To this end, we compared the performance of the deep-learning transformer-based model, bio-clinical-BERT, with a bag-of-words logistic regression model incorporating term frequency-inverse document frequency (BOW-LR-tf-idf). A retrospective analysis was conducted using data from 1,391,988 Emergency Department patients at the Mount Sinai Health System spanning 2017-2022. The models were trained on four hospitals’ data and externally validated on a fifth. Bio-clinical-BERT achieved higher AUCs (0.82, 0.84, and 0.85) compared to BOW-LR-tf-idf (0.81, 0.83, and 0.84) across training sets of 10,000, 100,000, and ∼1,000,000 patients respectively. Notably, both models proved effective at utilizing triage notes for prediction, despite the modest performance gap. Importantly, our findings suggest that simpler machine learning models like BOW-LR-tf-idf could serve adequately in resource-limited settings. Given the potential implications for patient care and hospital resource management, further exploration of alternative models and techniques is warranted to enhance predictive performance in this critical domain.