Dear Watch, Should I Get a COVID-19 Test? Designing deployable machine learning for wearables

  1. Bret Nestor
  2. Jaryd Hunter
  3. Raghu Kainkaryam
  4. Erik Drysdale
  5. Jeffrey B. Inglis
  6. Allison Shapiro
  7. Sujay Nagaraj
  8. Marzyeh Ghassemi
  9. Luca Foschini
  10. Anna Goldenberg

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Abstract

Commercial wearable devices are surfacing as an appealing mechanism to detect COVID-19 and potentially other public health threats, due to their widespread use. To assess the validity of wearable devices as population health screening tools, it is essential to evaluate predictive methodologies based on wearable devices by mimicking their real-world deployment. Several points must be addressed to transition from statistically significant differences between infected and uninfected cohorts to COVID-19 inferences on individuals. We demonstrate the strengths and shortcomings of existing approaches on a cohort of 32, 198 individuals who experience influenza like illness (ILI), 204 of which report testing positive for COVID-19. We show that, despite commonly made design mistakes resulting in overestimation of performance, when properly designed wearables can be effectively used as a part of the detection pipeline. For example, knowing the week of year, combined with naive randomised test set generation leads to substantial overestimation of COVID-19 classification performance at 0.73 AUROC. However, an average AUROC of only 0.55 ± 0.02 would be attainable in a simulation of real-world deployment, due to the shifting prevalence of COVID-19 and non-COVID-19 ILI to trigger further testing. In this work we show how to train a machine learning model to differentiate ILI days from healthy days, followed by a survey to differentiate COVID-19 from influenza and unspecified ILI based on symptoms. In a forthcoming week, models can expect a sensitivity of 0.50 (0-0.74, 95% CI), while utilising the wearable device to reduce the burden of surveys by 35%. The corresponding false positive rate is 0.22 (0.02-0.47, 95% CI). In the future, serious consideration must be given to the design, evaluation, and reporting of wearable device interventions if they are to be relied upon as part of frequent COVID-19 or other public health threat testing infrastructures.

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  1. ScreenIT

    SciScore for 10.1101/2021.05.11.21257052: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: There are several limitations of this study which would affect deployment-level performance. Most importantly, our training dataset does not contain participants who are healthy throughout the entire duration of the study. Upon deployment, the PPV, NPV, specificity, AUROC, and AUPR will all be affected since these metrics involve the calls made on COVID-19 negative participants. In addition, the participants consenting in these studies may not represent the population which this would be deployed for. Both cohorts are comprised of primarily female, and primarily white demographics. This could be attributed to the selection bias in those who wish to participate, or the selection bias of individuals who own a wearable device. We find that symptom-based screening substantially outperforms wearable device screening in differentiating between COVID-19 cases and non-COVID-19 ILI cases. The limitation of symptom screening is that it negates the ability of the wearable devices to identify positive COVID-19 cases prior to symptom onset. Both models could be improved from enriched features. For example changes in heart rate variability metrics have been shown to be associated with COVID-19 [19; 35; 43]. Our features are summarised on the day level. More granular time intervals could improve identification of ILI signatures. Improvements could be made to the modelling such as using side information from the survey data to improve the representational capacity of the wearabl...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.05.11.21257052v1 on medRxiv