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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study, being retrospective in nature, was reviewed and approved by an ethical institutional review board (IRB) committee from MSHS. The IRB committee deemed that due to the retrospective nature of the study, the requirement for informed consent was waived. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors