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Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study

BACKGROUND: A large number of web-based COVID-19 symptom checkers and chatbots have been developed; however, anecdotal evidence suggests that their conclusions are highly variable. To our knowledge, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner....

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Autores principales: Munsch, Nicolas, Martin, Alistair, Gruarin, Stefanie, Nateqi, Jama, Abdarahmane, Isselmou, Weingartner-Ortner, Rafael, Knapp, Bernhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541039/
https://www.ncbi.nlm.nih.gov/pubmed/33001828
http://dx.doi.org/10.2196/21299
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author Munsch, Nicolas
Martin, Alistair
Gruarin, Stefanie
Nateqi, Jama
Abdarahmane, Isselmou
Weingartner-Ortner, Rafael
Knapp, Bernhard
author_facet Munsch, Nicolas
Martin, Alistair
Gruarin, Stefanie
Nateqi, Jama
Abdarahmane, Isselmou
Weingartner-Ortner, Rafael
Knapp, Bernhard
author_sort Munsch, Nicolas
collection PubMed
description BACKGROUND: A large number of web-based COVID-19 symptom checkers and chatbots have been developed; however, anecdotal evidence suggests that their conclusions are highly variable. To our knowledge, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner. OBJECTIVE: The aim of this study is to evaluate and compare the diagnostic accuracies of web-based COVID-19 symptom checkers. METHODS: We identified 10 web-based COVID-19 symptom checkers, all of which were included in the study. We evaluated the COVID-19 symptom checkers by assessing 50 COVID-19 case reports alongside 410 non–COVID-19 control cases. A bootstrapping method was used to counter the unbalanced sample sizes and obtain confidence intervals (CIs). Results are reported as sensitivity, specificity, F1 score, and Matthews correlation coefficient (MCC). RESULTS: The classification task between COVID-19–positive and COVID-19–negative for “high risk” cases among the 460 test cases yielded (sorted by F1 score): Symptoma (F1=0.92, MCC=0.85), Infermedica (F1=0.80, MCC=0.61), US Centers for Disease Control and Prevention (CDC) (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Cleveland Clinic (F1=0.40, MCC=0.07), Providence (F1=0.40, MCC=0.05), Apple (F1=0.29, MCC=-0.10), Docyet (F1=0.27, MCC=0.29), Ada (F1=0.24, MCC=0.27) and Your.MD (F1=0.24, MCC=0.27). For “high risk” and “medium risk” combined the performance was: Symptoma (F1=0.91, MCC=0.83) Infermedica (F1=0.80, MCC=0.61), Cleveland Clinic (F1=0.76, MCC=0.47), Providence (F1=0.75, MCC=0.45), Your.MD (F1=0.72, MCC=0.33), CDC (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Apple (F1=0.70, MCC=0.25), Ada (F1=0.42, MCC=0.03), and Docyet (F1=0.27, MCC=0.29). CONCLUSIONS: We found that the number of correctly assessed COVID-19 and control cases varies considerably between symptom checkers, with different symptom checkers showing different strengths with respect to sensitivity and specificity. A good balance between sensitivity and specificity was only achieved by two symptom checkers.
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spelling pubmed-75410392020-10-20 Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study Munsch, Nicolas Martin, Alistair Gruarin, Stefanie Nateqi, Jama Abdarahmane, Isselmou Weingartner-Ortner, Rafael Knapp, Bernhard J Med Internet Res Original Paper BACKGROUND: A large number of web-based COVID-19 symptom checkers and chatbots have been developed; however, anecdotal evidence suggests that their conclusions are highly variable. To our knowledge, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner. OBJECTIVE: The aim of this study is to evaluate and compare the diagnostic accuracies of web-based COVID-19 symptom checkers. METHODS: We identified 10 web-based COVID-19 symptom checkers, all of which were included in the study. We evaluated the COVID-19 symptom checkers by assessing 50 COVID-19 case reports alongside 410 non–COVID-19 control cases. A bootstrapping method was used to counter the unbalanced sample sizes and obtain confidence intervals (CIs). Results are reported as sensitivity, specificity, F1 score, and Matthews correlation coefficient (MCC). RESULTS: The classification task between COVID-19–positive and COVID-19–negative for “high risk” cases among the 460 test cases yielded (sorted by F1 score): Symptoma (F1=0.92, MCC=0.85), Infermedica (F1=0.80, MCC=0.61), US Centers for Disease Control and Prevention (CDC) (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Cleveland Clinic (F1=0.40, MCC=0.07), Providence (F1=0.40, MCC=0.05), Apple (F1=0.29, MCC=-0.10), Docyet (F1=0.27, MCC=0.29), Ada (F1=0.24, MCC=0.27) and Your.MD (F1=0.24, MCC=0.27). For “high risk” and “medium risk” combined the performance was: Symptoma (F1=0.91, MCC=0.83) Infermedica (F1=0.80, MCC=0.61), Cleveland Clinic (F1=0.76, MCC=0.47), Providence (F1=0.75, MCC=0.45), Your.MD (F1=0.72, MCC=0.33), CDC (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Apple (F1=0.70, MCC=0.25), Ada (F1=0.42, MCC=0.03), and Docyet (F1=0.27, MCC=0.29). CONCLUSIONS: We found that the number of correctly assessed COVID-19 and control cases varies considerably between symptom checkers, with different symptom checkers showing different strengths with respect to sensitivity and specificity. A good balance between sensitivity and specificity was only achieved by two symptom checkers. JMIR Publications 2020-10-06 /pmc/articles/PMC7541039/ /pubmed/33001828 http://dx.doi.org/10.2196/21299 Text en ©Nicolas Munsch, Alistair Martin, Stefanie Gruarin, Jama Nateqi, Isselmou Abdarahmane, Rafael Weingartner-Ortner, Bernhard Knapp. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.10.2020. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Munsch, Nicolas
Martin, Alistair
Gruarin, Stefanie
Nateqi, Jama
Abdarahmane, Isselmou
Weingartner-Ortner, Rafael
Knapp, Bernhard
Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study
title Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study
title_full Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study
title_fullStr Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study
title_full_unstemmed Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study
title_short Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study
title_sort diagnostic accuracy of web-based covid-19 symptom checkers: comparison study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541039/
https://www.ncbi.nlm.nih.gov/pubmed/33001828
http://dx.doi.org/10.2196/21299
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