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Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
Published in: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine 1/2020

01-12-2020 | Artificial Intelligence | Original research

Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data

Authors: Thomas Langer, Martina Favarato, Riccardo Giudici, Gabriele Bassi, Roberta Garberi, Fabiana Villa, Hedwige Gay, Anna Zeduri, Sara Bragagnolo, Alberto Molteni, Andrea Beretta, Matteo Corradin, Mauro Moreno, Chiara Vismara, Carlo Federico Perno, Massimo Buscema, Enzo Grossi, Roberto Fumagalli

Published in: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine | Issue 1/2020

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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.

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.
Appendix
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Metadata
Title
Development of machine learning models to predict RT-PCR results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data
Authors
Thomas Langer
Martina Favarato
Riccardo Giudici
Gabriele Bassi
Roberta Garberi
Fabiana Villa
Hedwige Gay
Anna Zeduri
Sara Bragagnolo
Alberto Molteni
Andrea Beretta
Matteo Corradin
Mauro Moreno
Chiara Vismara
Carlo Federico Perno
Massimo Buscema
Enzo Grossi
Roberto Fumagalli
Publication date
01-12-2020
Publisher
BioMed Central
DOI
https://doi.org/10.1186/s13049-020-00808-8

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