@article{ref1,
title="Predicting hospital admission from emergency department triage data for patients presenting with fall-related fractures",
journal="Internal and emergency medicine",
year="2022",
author="Pai, Dinesh R. and Rajan, Balaraman and Jairath, Puneet and Rosito, Stephen M.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="PURPOSE: Predict in advance the need for hospitalization of adult patients for fall-related fractures based on information available at the time of triage to help decision-making at the emergency department (ED). <br><br>METHODS: We developed machine learning models using routinely collected triage data at a regional hospital chain in Pennsylvania to predict admission to an inpatient unit. We considered all patients presenting to the ED for fall-related fractures. Patients who were 18 years or younger, who left the ED against medical advice, left the ED waiting room without being seen by a provider, and left the ED after initial diagnostics were excluded from the analysis. We compared models obtained using triage data (pre-model) with models developed using additional data obtained after physicians' diagnoses (post-model). <br><br>RESULTS: Our results show good discriminatory power on predicting hospital admissions. Neural network models performed the best (AUC: pre-model = 0.938 [CI 0.920-0.956], post-model = 0.983 [0.974-0.992]). The logistic regression analysis provides additional insights into the data and the relationships between the variables. <br><br>CONCLUSIONS: Using limited data available at the time of triage, we developed four machine learning models aimed at predicting hospitalization for patients presenting to the ED for fall-related fractures. All the four models were robust and performed well. Neural network method, however, performed the best for both pre- and post-models. Simple, parsimonious machine learning models can provide high accuracy for predicting hospital admission.<p /> <p>Language: en</p>",
language="en",
issn="1828-0447",
doi="10.1007/s11739-022-03100-y",
url="http://dx.doi.org/10.1007/s11739-022-03100-y"
}