| Autor: |
Botonis, Olivia K., Mendley, Jonathan, Aalla, Shreya, Veit, Nicole C., Fanton, Michael, Lee, JongYoon, Tripathi, Vikrant, Pandi, Venkatesh, Khobragade, Akash, Chaudhary, Sunil, Chaudhuri, Amitav, Narayanan, Vaidyanathan, Xu, Shuai, Jeong, Hyoyoung, Rogers, John A., Jayaraman, Arun |
| Zdroj: |
NPJ Digital Medicine; 10/19/2024, Vol. 7 Issue 1, p1-12, 12p |
| Abstrakt: |
The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022). An Extreme Gradient Boosting algorithm was trained to discriminate between COVID-19 infected individuals (n = 295) and COVID-19 negative healthy controls (n = 172) and achieved an F1-Score of 0.80 (95% CI = [0.79, 0.81]). SHAP values were mapped to visualize feature importance and directionality, yielding engineered features from core temperature, cough, and lung sounds as highly important. The results demonstrated potential for data-driven wearable sensor technology for remote preliminary screening, highlighting a fundamental pivot from continuous to snapshot monitoring of cardiorespiratory illnesses. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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