The research team trained an AI algorithm using clinical data to better triage patients in low-testing environments and identify the infection at the point of care, as standard screening methods often take up to 48 hours to produce a diagnosis.
Routine blood tests conducted during the patient’s arrival, blood gas results and vital signs were used to train the algorithm.
The AI model used for diagnosis among COVID-19-presenting patients in the emergency department resulted in 77 percent sensitivity and 96 percent specificity, while the model used for diagnosis among patients admitted with COVID-19 resulted in 77 percent sensitivity and 95 percent specificity. More than 99 percent of patients the algorithm diagnosed as negative were in fact free of the disease.
Ecditor’s note: This article was updated July 14 at 9:00 a.m. CDT.
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