Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Objectives: We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device.
Design: Interim analysis of a prospective cohort study.
Setting, Participants And Interventions: Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays.
Results: A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO.
Conclusion: Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. ISRCTN51255782; Pre-results.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240454 | PMC |
| http://dx.doi.org/10.1136/bmjopen-2021-058274 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Professional development for Indonesian elementary school teachers: Increased competency and sustainable teacher development programs.
F1000Res
September 2025
Department Early Childhood Teacher Education, Universitas Muhammadiyah Jakarta, Jakarta, Indonesia.
Background: Various problems, such as a shortage of teachers, especially in certain outlying and underdeveloped provinces, unequal distribution of teachers, low teacher competency, and mismatch between educational qualifications and scientific fields, as well as the demands of increasingly dynamic developments in science and technology, resulting in the need for professional development for elementary school teachers to improve teacher professionalism in Indonesia. These background as a basis for the government to create teacher professional education program for elementary school teachers continuously. It is hoped that it will be able to answer the educational problems facing the Indonesian nation.
View Article and Find Full Text PDFRisk factors and trajectories for self-harm, neurodevelopmental disorders and mental health conditions in pupils in alternative education provision in Wales: population-based electronic cohort study.
BJPsych Open
September 2025
Swansea University Medical School, Swansea University, UK.
Background: Pupils in alternative education provision, known as 'Educated in Other Than At School' (EOTAS) in Wales, UK, are among the most vulnerable learners and who, for reasons such as mental health or behavioural challenges, do not attend a mainstream or special school.
Aims: We compared self-harm, neurodevelopmental disorders and mental health conditions between EOTAS pupils and controls with similar characteristics, before and after being in EOTAS provision.
Method: This population-based electronic cohort study included pupils in Wales aged 7-18 years, from the academic years 2010-11 to 2018-19.
MOLECULE: Molecular-dynamics and Optimized deep Learning for Entropy-regularized Classification and Uncertainty-aware Ligand Evaluation.
J Chem Theory Comput
September 2025
Dipartimento di Chimica, Università di Pavia, Via Taramelli 12, Pavia 27100, Italy.
Machine learning (ML) and deep learning (DL) methodologies have significantly advanced drug discovery and design in several aspects. Additionally, the integration of structure-based data has proven to successfully support and improve the models' predictions. Indeed, we previously demonstrated that combining molecular dynamics (MD)-derived descriptors with ML models allows to effectively classify kinase ligands as allosteric or orthosteric.
View Article and Find Full Text PDFQuality Difference Analysis of Pomegranate Peels Based on Multidimensional Fingerprint With Chemical Pattern Recognition.
Biomed Chromatogr
October 2025
College of Food Science and Pharmaceutical Engineering, Zaozhuang University, Zaozhuang, China.
To evaluate the quality of pomegranate peels from different cultivars, pomegranate peel samples from 47 cultivars were compared and classified based on fingerprints and chemical components obtained using HPLC-PDA-MS/MS combined with chemometric methods. Three pattern recognition methods, namely, hierarchical cluster analysis, principal component analysis, and partial least square-discriminant analysis, were used to establish classification models. Results showed that the contents of 10 components from pomegranate peel were determined.
View Article and Find Full Text PDFLeveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data.
ACS Sens
September 2025
Institute of Applied Mechanics, National Taiwan University, Taipei 106, Taiwan.
In recent AI-driven disease diagnosis, the success of models has depended mainly on extensive data sets and advanced algorithms. However, creating traditional data sets for rare or emerging diseases presents significant challenges. To address this issue, this study introduces a direct-self-attention Wasserstein generative adversarial network (DSAWGAN) designed to improve diagnostic capabilities in infectious diseases with limited data availability.
View Article and Find Full Text PDF