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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedStonko DP, Weller JH, Gonzalez Salazar AJ
A pilot machine learning study using trauma admission data to identify risk for high length of stay.
The purpose of this study was to design a tool that used only data available at time of admission for trauma to predict prolonged hospital length of stay (LOS). Data was collected from the trauma registry at an urban level-one adult trauma center. Single layer and deep artificial neural networks were trained to identify patients in the top quartile of LOS and optimized under the receiver operator characteristic curve. The results indicated that machine learning can predict which trauma patients will have prolonged LOS with physiologic and demographic data available at the time of admission. The authors concluded these patients may benefit from additional disposition planning resources at the time of admission.
AHRQ-funded; HS026640; HS024547; HS027793.
Citation: Stonko DP, Weller JH, Gonzalez Salazar AJ .
A pilot machine learning study using trauma admission data to identify risk for high length of stay.
Surg Innov 2023 Jun; 30(3):356-65. doi: 10.1177/15533506221139965..
Keywords: Trauma, Hospitalization, Health Information Technology (HIT)