Selected article for: "covid case and large number"

Author: Munsch, N.; Martin, A.; Gruarin, S.; Nateqi, J.; Abdarahmane, I.; Weingartner-Ortner, R.; Knapp, B.
Title: A benchmark of online COVID-19 symptom checkers
  • Cord-id: tmuvbq1k
  • Document date: 2020_5_26
  • ID: tmuvbq1k
    Snippet: Background A large number of online COVID-19 symptom checkers and chatbots have been developed but 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. Methods In this paper, we evaluate 10 different COVID-19 symptom checkers screening 50 COVID-19 case reports alongside 410 non-COVID-19 control cases. Results We find that the number of correctly assessed cases
    Document: Background A large number of online COVID-19 symptom checkers and chatbots have been developed but 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. Methods In this paper, we evaluate 10 different COVID-19 symptom checkers screening 50 COVID-19 case reports alongside 410 non-COVID-19 control cases. Results We find that the number of correctly assessed cases varies considerably between different symptom checkers, with Symptoma (F1=0.92, MCC=0.85) showing the overall best performance followed by Infermedica (F1=0.80, MCC=0.61).

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