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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
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...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7705856/ https://www.ncbi.nlm.nih.gov/pubmed/33261629 http://dx.doi.org/10.1186/s13049-020-00808-8 |
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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 |
author_facet | 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 |
author_sort | Langer, Thomas |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13049-020-00808-8. |
format | Online Article Text |
id | pubmed-7705856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77058562020-12-01 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 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 Scand J Trauma Resusc Emerg Med Original Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13049-020-00808-8. BioMed Central 2020-12-01 /pmc/articles/PMC7705856/ /pubmed/33261629 http://dx.doi.org/10.1186/s13049-020-00808-8 Text en © The Author(s) 2020 Open AccessThis 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 intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Original Research 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 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 |
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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7705856/ https://www.ncbi.nlm.nih.gov/pubmed/33261629 http://dx.doi.org/10.1186/s13049-020-00808-8 |
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