Dinesh R Pai1, Balaraman Rajan2, Puneet Jairath3, Stephen M Rosito4. 1. School of Business Administration, Penn State Harrisburg, 777 West Harrisburg Pike, Middletown, PA, 17057, USA. 2. Department of Management, College of Business and Economics, California State University East Bay, VBT 326, 25800 Carlos Bee Blvd, Hayward, CA, 94542, USA. balaraman.rajan@csueastbay.edu. 3. Department of Pediatrics, Office of Newborn Medicine, WellSpan Health, York Hospital, 1001 S George St, York, PA, 17403, USA. 4. School of Public Affairs, Penn State Harrisburg, 777 West Harrisburg Pike, Middletown, PA, 17057, USA.
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). 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). 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. 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.
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). 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). 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. 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.
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