NDLI: 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

Content Provider	Springer Nature : BioMed Central
Author	Langer, Thomas Favarato, Martina Giudici, Riccardo Bassi, Gabriele Garberi, Roberta Villa, Fabiana Gay, Hedwige Zeduri, Anna Bragagnolo, Sara Molteni, Alberto Beretta, Andrea Corradin, Matteo Moreno, Mauro Vismara, Chiara Perno, Carlo Federico Buscema, Massimo Grossi, Enzo Fumagalli, Roberto
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.
Related Links	https://sjtrem.biomedcentral.com/counter/pdf/10.1186/s13049-020-00808-8.pdf
Ending Page	14
Page Count	14
Starting Page	1
File Format	HTM / HTML
ISSN	17577241
DOI	10.1186/s13049-020-00808-8
Journal	Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
Issue Number	1
Volume Number	28
Language	English
Publisher	BioMed Central
Publisher Date	2020-12-01
Access Restriction	Open
Subject Keyword	Scandinavian Journal of Trauma Resuscitation and Emergency Medicine Emergency Medicine Traumatic Surgery Severe acute respiratory syndrome coronavirus 2 Emergency service hospital Pandemics Critical care Artificial intelligence Supervised machine learning
Content Type	Text
Resource Type	Article
Subject	Emergency Medicine Critical Care and Intensive Care Medicine
Journal Impact Factor	3/2023
5-Year Journal Impact Factor	3.5/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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