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PMID: 33001828 Published · epublish English Comparative Study Journal Article Research Support, Non-U.S. Gov't

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

Journal of medical Internet research ·Vol. 22 ·No. 10 ·2020-00-06 ·页码 e21299

Munsch N, Martin A, Gruarin S, Nateqi J, Abdarahmane I, Weingartner-Ortner R, Knapp B

Abstract

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. The aim of this study is to evaluate and compare the diagnostic accuracies of web-based COVID-19 symptom checkers. 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). 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). 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.

Keywords
COVID-19 accuracy benchmark chatbot digital health symptom symptom checkers
MeSH 主题词
Adolescent Adult Algorithms Betacoronavirus COVID-19 COVID-19 Testing Centers for Disease Control and Prevention, U.S. Clinical Laboratory Techniques Coronavirus Infections/diagnosis,epidemiology Data Collection Diagnostic Self Evaluation Humans Internet Middle Aged Pandemics Pneumonia, Viral/diagnosis,epidemiology Predictive Value of Tests Public Health Informatics Reproducibility of Results SARS-CoV-2 Self Report Sensitivity and Specificity Symptom Assessment/instrumentation United States Young Adult
作者与单位
共 7 位作者,点击展开单位 / ORCID
Munsch Nicolas ORCID
Data Science Department, Symptoma, Vienna, Austria.
Martin Alistair ORCID
Data Science Department, Symptoma, Vienna, Austria.
Gruarin Stefanie ORCID
Medical Department, Symptoma, Attersee, Austria.
Nateqi Jama ORCID
Medical Department, Symptoma, Attersee, Austria. | Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria.
Abdarahmane Isselmou ORCID
Data Science Department, Symptoma, Vienna, Austria.
Weingartner-Ortner Rafael ORCID
Data Science Department, Symptoma, Vienna, Austria. | Medical Department, Symptoma, Attersee, Austria.
Knapp Bernhard ORCID
Data Science Department, Symptoma, Vienna, Austria.
Article Info
Journal
Journal of medical Internet research
Abbr.
J Med Internet Res
ISSN
1438-8871
Published
2020-00-06
电子出版
2020-00-06
页码
e21299
Language
English
Country/Region
Canada
NLM ID
100959882
勘误 / 撤稿关联
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