Unknown

Dataset Information

0

Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients.

ABSTRACT: Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59?±?0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r?=?0.96, p?

SUBMITTER: Un KC 

PROVIDER: S-EPMC7902655 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

Json Xml
altmetric image

Publications

Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59  ...[more]

Similar Datasets

Unknown Highly flexible, wearable, and disposable cardiac biosensors for remote and ambulatory monitoring.
| S-EPMC6550217 | biostudies-literature
Unknown Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients.
| S-EPMC8576003 | biostudies-literature
Unknown Stress Monitoring and Recent Advancements in Wearable Biosensors.
| S-EPMC7492543 | biostudies-literature
Unknown Wearable Contact Lens Biosensors for Continuous Glucose Monitoring Using Smartphones.
| S-EPMC6107296 | biostudies-literature
Unknown Machine learning-based prediction of COVID-19 diagnosis based on symptoms.
| S-EPMC7782717 | biostudies-literature
Unknown Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.
| S-EPMC8424152 | biostudies-literature
Unknown A machine learning based exploration of COVID-19 mortality risk.
| S-EPMC8253432 | biostudies-literature
Unknown Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning.
| S-EPMC8719000 | biostudies-literature
Unknown Remote COVID-19 patient monitoring system: a qualitative evaluation.
| S-EPMC9072784 | biostudies-literature
Unknown Wearable Sensors for COVID-19: A Call to Action to Harness Our Digital Infrastructure for Remote Patient Monitoring and Virtual Assessments
| S-EPMC8521919 | biostudies-literature