Classification of Patient Recovery from COVID-19 Symptoms using Consumer Wearables and Machine Learning

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 3; pp. 1 - 12
• Main Authors: Leitner, Jared, Behnke, Alexander, Chiang, Po-Han, Ritter, Michele, Millen, Marlene, Dey, Sujit
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applications programs; Coronaviruses; COVID-19; Data collection; Heart rate; Learning algorithms; Machine learning; Medical services; Mobile computing; Patient compliance; Predictive models; Recovery (Medical); Remote monitoring; Remote Patient Monitoring; Wearable computers; Wearable technology; Wearables
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2023.3239366

Current remote monitoring of COVID-19 patients relies on manual symptom reporting, which is highly dependent on patient compliance. In this research, we present a machine learning (ML)-based remote monitoring method to estimate patient recovery from COVID-19 symptoms using automatically collected wearable device data, instead of relying on manually collected symptom data. We deploy our remote monitoring system, namely eCOVID, in two COVID-19 telemedicine clinics. Our system utilizes a Garmin wearable and symptom tracker mobile app for data collection. The data consists of vitals, lifestyle, and symptom information which is fused into an online report for clinicians to review. Symptom data collected via our mobile app is used to label the recovery status of each patient daily. We propose a ML-based binary patient recovery classifier which uses wearable data to estimate whether a patient has recovered from COVID-19 symptoms. We evaluate our method using leave-one-subject-out (LOSO) cross-validation, and find that Random Forest (RF) is the top performing model. Our method achieves an F1-score of 0.88 when applying our RF-based model personalization technique using weighted bootstrap aggregation. Our results demonstrate that ML-assisted remote monitoring using automatically collected wearable data can supplement or be used in place of manual daily symptom tracking which relies on patient compliance.