Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SA
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ORIGINAL RESEARCH
Open Access
Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) in patients with influenza-like symptoms using only basic clinical data Thomas Langer1,2*† , Martina Favarato1,2†, Riccardo Giudici2, Gabriele Bassi2, Roberta Garberi1, Fabiana Villa1, Hedwige Gay1,2, Anna Zeduri1, Sara Bragagnolo1, Alberto Molteni3, Andrea Beretta4, Matteo Corradin5, Mauro Moreno5, Chiara Vismara6, Carlo Federico Perno6, Massimo Buscema7,8, Enzo Grossi9,10 and Roberto Fumagalli1,2
Abstract Background: 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 main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2, using basic information at hand in all emergency departments. Methods: This is a retrospective study carried out between February 22, 2020 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 and patients in whom the leukocyte formula was not 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. Different Machine Learning algorithms available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol. 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. (Continued on next page)
* Correspondence: [email protected] † Thomas Langer and Martina Favarato contributed equally to this work. 1 Department of Medicine and Surgery, University of Milan-Bicocca, Monza, Italy 2 Department of Anaesthesia and Intensive Care Medicine, Niguarda Ca’ Granda, Milan, Italy Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your in
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