Predicting Pediatric Length of Stay and Acuity of Care in the First Ten Minutes with Artificial Neural Networks

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artificial neural network, artificial intelligence, neu-ral networks (computer), length of stay, trauma, pediatric trauma, hospitalization

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Objective: To evaluate the efficacy of artificial neural networks in categorizing pediatric trauma patients into four distinct acuity of care groups and in determining the length of stay (LOS) within specific areas of the hospital.

Design: Using historical information from > 8,000 pediatric trauma patient records, train and evaluate artificial neural networks to predict the injury severity and LOS for each patient in pediatric intensive care units (PICUs), step-down units, and floor units. Each artificial neural network is evaluated for categorization accuracy and mean absolute error difference on the predicted LOS.

Subjects: A total of 10,353 patient records from the National Pediatric Trauma registry, representing all pediatric trauma patients treated at affiliated hospitals from April 1994 through December 1996. Records with incomplete information were eliminated from the study, leaving 8,081 usable patient records.

Measurements: A total of 14 variables are selected from the 81 values present in the National Pediatric Trauma Registry as independent variables for the artificial neural networks. Each neural network produces nine output values: five for categorizing the patient’s injury severity, three for the LOS in the PICU, step-down unit, and floor units, and one for the patient’s total LOS.

Results: A fuzzy ARTMAP neural network accurately categorizes 88% of mortality patients and 58.3% of critical PICU patients. A backpropagation neural network succeeded in predicting the total LOS to within 1 day for 51.4% and the ICU LOS to within 1 day for 70.4% of all evaluated patients.

Conclusion: Information available in the first 10 mins of a patient’s presentation at the emergency room can be used by an artificial neural network to predict injury severity and LOS. Artificial neural networks enable more effective resource planning and patient management.

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Citation / Publisher Attribution

Pediatric Critical Care Medicine, v. 1, issue 1, p. 42-47