Use of Machine Learning to Rapidly Predict Positivity to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) Using Basic Clinical Data

Abstract Objective: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our maingoal was assessing the accuracy of artificial intelligence in forecasting the resultsof RT-PCR for SARS-COV-2, using basic information at hand in all emergencydepartments.Methods: This is a retrospective study carried out between February 22 and March 16 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2.Patients under 12 years old, with no leukocyte formula performed in the ED,were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission.Results: Among 199 patients subject to study (median [interquartile range] age 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity and 88.7% specificity.Conclusion: Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2, using basic clinical data. If confirmed,on a larger-scale study, this approach could have important clinical and organizational implications.