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Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
University 2020 Foundation, Northborough, MA 01532, USA
Department of Mechanical Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
Institute for Communications (ICS), 5G&6G Innovation Centre (5G&6GIC), University of Surrey, Guildford GU2 7XH, UK
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Health Informatics and Epidemiological Data Analysis in COVID-19 Based Internet of Medical Things (HIEDA-COVID19-IOMT)

Abstract submission deadline
15 October 2023
Manuscript submission deadline
15 December 2023
Viewed by
1347

Topic Information

Dear Colleagues,

In the presence of external environmental factors, COVID-19 has become a worldwide epidemic problem. The journal of the National Academy of Sciences of the United States calculates that the total weight of COVID-19 virus particles is about 100 grams to 10 kilograms, which is between an apple and a child. It is just like a nuclear bomb, with a small mass but capable of devastating destruction. The study stated that each coronavirus particle had a mass of 1 nano coronavirus gram, which calculated that each infected person carried about 1 to 10 micrograms of virus particles at the peak of infection, based on the estimated 10 to 100 billion coronavirus particles per infected person at the peak of infection. IoMT medical applications can also help the patients of COVID-19 keep in touch with doctors or nurses. The intelligent detection means using the Internet of Things technology sends various physical data of patients in real time through the communication module, and at the same time has the optimization of clinical basic nursing work, such as automatic recording, summary, and alarm. In order to further improve the hospitalization experience of patients and improve the quality of medical services, 5G technology and related smart hardware devices based on the Internet, artificial intelligence, and the Internet of Things are used to build mobile nursing, physical sign monitoring, wireless infusion monitoring, and mobile rounds under 5G technology. Then combined with deep learning to analyze and integrate medical data to optimize management methods and rationally allocate medical resources. This Special Issue is aimed at presenting the state-of-the-art, current challenges and future trends for the successful application of artificial intelligence in management of epidemic control of COVID-19 using the IoMT platform. Original contributions considering recent findings in theory, methodologies, and applications in the field of artificial Intelligence and Internet computing in COVID-19 are welcome.

Prof. Dr. Wenjun (Chris) Zhang
Prof. Dr. Dhanjoo N. Ghista
Prof. Dr. Kelvin K.L. Wong
Prof. Dr. Zhili Sun
Topic Editors

Keywords

  • artificial intelligence techniques using the HIEDA-COVID19-IOMT in medical imaging
  • deep learning models based on HIEDA-COVID19-IOMT
  • deep learning based HIEDA-COVID19-IOMT for medical image classification, regression, localization, and segmentation
  • content-based medical image retrieval
  • computer-aided detection and diagnosis based on HIEDA-COVID19-IOMT

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
BioMed
biomed 		- - 2021 24.6 Days 1000 CHF Submit
Biomedicines
biomedicines 		4.757 3.0 2013 17.4 Days 2200 CHF Submit
BioMedInformatics
biomedinformatics 		- - 2021 10.7 Days 1000 CHF Submit
Epidemiologia
epidemiologia 		- - 2020 25.3 Days 1000 CHF Submit
Journal of Clinical Medicine
jcm 		4.964 4.4 2012 18 Days 2600 CHF Submit

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Published Papers (1 paper)

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Article
Rapid Assessment of COVID-19 Mortality Risk with GASS Classifiers
by
Salvatore Greco
,
Alessandro Salatiello
,
Nicolò Fabbri
,
Fabrizio Riguzzi
,
Emanuele Locorotondo
,
Riccardo Spaggiari
,
Alfredo De Giorgi
and
Angelina Passaro
Biomedicines 2023, 11(3), 831; https://doi.org/10.3390/biomedicines11030831 - 09 Mar 2023
Viewed by 656
Abstract
Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can [...] Read more.
Risk prediction models are fundamental to effectively triage incoming COVID-19 patients. However, current triaging methods often have poor predictive performance, are based on variables that are expensive to measure, and often lead to hard-to-interpret decisions. We introduce two new classification methods that can predict COVID-19 mortality risk from the automatic analysis of routine clinical variables with high accuracy and interpretability. SVM22-GASS and Clinical-GASS classifiers leverage machine learning methods and clinical expertise, respectively. Both were developed using a derivation cohort of 499 patients from the first wave of the pandemic and were validated with an independent validation cohort of 250 patients from the second pandemic phase. The Clinical-GASS classifier is a threshold-based classifier that leverages the General Assessment of SARS-CoV-2 Severity (GASS) score, a COVID-19-specific clinical score that recently showed its effectiveness in predicting the COVID-19 mortality risk. The SVM22-GASS model is a binary classifier that non-linearly processes clinical data using a Support Vector Machine (SVM). In this study, we show that SMV22-GASS was able to predict the mortality risk of the validation cohort with an AUC of 0.87 and an accuracy of 0.88, better than most scores previously developed. Similarly, the Clinical-GASS classifier predicted the mortality risk of the validation cohort with an AUC of 0.77 and an accuracy of 0.78, on par with other established and emerging machine-learning-based methods. Our results demonstrate the feasibility of accurate COVID-19 mortality risk prediction using only routine clinical variables, readily collected in the early stages of hospital admission. Full article
(This article belongs to the Topic Health Informatics and Epidemiological Data Analysis in COVID-19 Based Internet of Medical Things (HIEDA-COVID19-IOMT))
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