Dear Watch, Should I get a COVID Test? Designing deployable machine learning for wearables

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.

Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset

“…Because the outcome labeling is robust and accurate, there is a significant reduction in noise that would be present in an observational study. 41 The participants in both studies experienced clinically mild disease, so the physiologic changes in patients with severe disease outcomes would likely be even more extreme and therefore easier to detect. The timing of the models' detection and severity prediction is particularly relevant to current work aimed at early detection of COVID-19 from smartwatches, as presymptomatic and asymptomatic spread are significant contributors to the SARS-CoV-2 pandemic.…”

“…Two factors associated with model accuracy are (1) knowledge of the exact time and dosage of inoculation and (2) the high-fidelity measurements of the research-grade wearable that enable intricate feature engineering, neither of which are possible in existing observational studies using consumer-grade devices. Because the outcome labeling is robust and accurate, there is a significant reduction in noise that would be present in an observational study . The participants in both studies experienced clinically mild disease, so the physiologic changes in patients with severe disease outcomes would likely be even more extreme and therefore easier to detect.…”