Machine learning-based optimization of pre-symptomatic COVID-19 detection through smartwatch

Cho, Hyeong Rae; Kim, Jin Hyun; Yoon, Hye Rin; Han, Yong Seop; Kang, Tae Seen; Choi, Hyunju; Lee, Seung Hwan

doi:10.1038/s41598-022-11329-y

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…For instance, Mishara et al reported a comprehensive study where physiological data from smartwatches were used to predict COVID-19 presymptomatic cases [ 90 ]. Inspired by this work, many machine learning algorithms have been reported in recent studies [ 91 , 92 ] and are equally important for MPXV detection. It is important to know the virus’s infectiousness status after infection.…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

Current and Perspective Sensing Methods for Monkeypox Virus

et al. 2022

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The outbreak of the monkeypox virus (MPXV) in non-endemic countries is an emerging global health threat and may have an economic impact if proactive actions are not taken. As shown by the COVID-19 pandemic, rapid, accurate, and cost-effective virus detection techniques play a pivotal role in disease diagnosis and control. Considering the sudden multicountry MPXV outbreak, a critical evaluation of the MPXV detection approaches would be a timely addition to the endeavors in progress for MPXV control and prevention. Herein, we evaluate the current MPXV detection methods, discuss their pros and cons, and provide recommended solutions to the problems. We review the traditional and emerging nucleic acid detection approaches, immunodiagnostics, whole-particle detection, and imaging-based MPXV detection techniques. The insights provided in this article will help researchers to develop novel techniques for the diagnosis of MPXV.

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Section: Conclusion and Prospectsmentioning

confidence: 99%

Current and Perspective Sensing Methods for Monkeypox Virus

et al. 2022

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“…LSTM has also been used for detecting congestive heart failure [ 116 ]. Cho et al claimed that one-class SVM provided a 23.5–40% earlier detection of COVID-19 compared to the LSTM method [ 79 ]. LSTM has also been applied for estimating sleep stages from wearable data [ 117 ].…”

Section: Role Of Machine Learning In Diagnosticsmentioning

confidence: 99%

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

Chakrabarti¹,

Biswas²,

Jones³

et al. 2022

Diagnostics

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The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

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“…Cho et al. improved upon that study by proposing a One Class-Support Vector Machine (OC-SVM) for pre-symptomatic COVID-19 detection ( Cho et al, 2022 ). Their method improved the presymptomatic detection performance over the statistical method proposed by Mishra et al.…”

Section: Introductionmentioning

confidence: 99%