Study: ML Algorithm Helps Detect Traumatic Intracranial Hemorrhage using Prehospital Data

Study: ML Algorithm Helps Detect Traumatic Intracranial Hemorrhage using Prehospital Data

A machine learning algorithm can accurately detect traumatic intracranial hemorrhage using information collected before patients reach the hospital, according to a study published in JAMA Network Open. The study analyzed electronic health records from 2,123 patients with head trauma who were transported to Tokyo Medical and Dental University Hospital from April 1, 2018, to March 31, 2021. "The results suggest that our proposed prediction models may be useful for constructing a triage system that can be used to assess the optimal institution to which a patient with a head injury should be transported. "As the functional outcomes of patients with head injury worsen when their transportation is delayed, the transport time in step three should be reduced by constructing a reliable field triage tool," the researchers wrote. "Because this was a single-center study and included only patients who were hospitalized and underwent head CT, our data set may not represent the general population of patients with head trauma," they wrote.




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