Artificial Intelligence Can Predict Daily Trauma Volume and Average Acuity

Introduction The goal of this study was to integrate temporal and weather data in order to create an artificial neural network (ANN) to predict trauma volume, the number of emergent operative cases, and average daily acuity at a level 1 trauma center. Methods Trauma admission data from TRACS and weather data from the National Oceanic and Atmospheric Administration (NOAA) was collected for all adult trauma patients from July 2013-June 2016. The ANN was constructed using temporal (time, day of week), and weather factors (daily high, active precipitation) to predict four points of daily trauma activity: number of traumas, number of penetrating traumas, average ISS, and number of immediate OR cases per day. We trained a two-layer feed-forward network with 10 sigmoid hidden neurons via the Levenberg-Marquardt backpropagation algorithm, and performed k-fold cross validation and accuracy calculations on 100 randomly generated partitions. Results 10,612 patients over 1,096 days were identified. The ANN accurately predicted the daily trauma distribution in terms of number of traumas, number of penetrating traumas, number of OR cases, and average daily ISS (combined training correlation coefficient r = 0.9018+/-0.002; validation r = 0.8899+/- 0.005; testing r = 0.8940+/-0.006). Conclusion We were able to successfully predict trauma and emergent operative volume, and acuity using an ANN by integrating local weather and trauma admission data from a level 1 center. As an example, for June 30, 2016, it predicted 9.93 traumas (actual: 10), and a mean ISS score of 15.99 (actual: 13.12); see figure 3. This may prove useful for predicting trauma needs across the system and hospital administration when allocating limited resources. Evidence Level Level III Study Type Prognostic/Epidemiological 48th Annual Western Trauma Association Meeting, February 26, 2018 in Whistler, BC, Canada Correspondence to: Oscar.Guillamondegui@Vanderbilt.edu, 1211 21st Ave S #404, Nashville, TN 37212Conflict of Interest: There was no support from pharmaceutical or other industry, or from grants or other sources of funding. © 2018 Lippincott Williams & Wilkins, Inc.

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