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Development of a Machine Learning Model to Predict Cardiac Arrest during Transport of Trauma Patients
8
Zitationen
14
Autoren
2023
Jahr
Abstract
BACKGROUND: Trauma is a serious medical and economic burden worldwide, and patients with traumatic injuries have a poor survival rate after cardiac arrest. The authors developed a prediction model specific to prehospital trauma care and used machine learning techniques to increase its accuracy. METHODS: This retrospective observational study analyzed data from patients with blunt trauma injuries due to traffic accidents and falls from January 1, 2018, to December 31, 2019. The data were collected from the National Emergency Medical Services Information System, which stores emergency medical service activity records nationwide in the United States. A random forest algorithm was used to develop a machine learning model. RESULTS: in nonalert patients. CONCLUSIONS: The machine learning model was highly accurate in identifying patients who did not develop cardiac arrest.
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