ArticleArtificial intelligence in medicine2022
Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control.
Article in Artificial intelligence in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 26 citations in OpenAlex.
- The role of artificial intelligence in managing COVID-19 and long COVID: a narrative review.Osong public health and research perspectives · 2026Article
- Advancing infection prevention and control through artificial intelligence: a scoping review of applications, barriers, and a decision-support checklist.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2025Article
- Innovative applications of artificial intelligence during the COVID-19 pandemic.Infectious medicine · 2024Review
- Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.Neural computing & applications · 2023Review
- A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection.Expert systems with applications · 2022Article
- Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.Artificial intelligence in medicine · 2022Article
- Processing Decision Tree Data Using Internet of Things (IoT) and Artificial Intelligence Technologies with Special Reference to Medical Application.BioMed research international · 2022Review
- How to design digital health interventions with artificial intelligence: A scoping review.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is a life-threatening contagious virus that has spread across the globe rapidly. To reduce the outbreak impact of COVID-19 virus illness, continual identification and remote surveillance of patients are essential. Medical service delivery based on the Internet of Things (IoT) technology backed up by the fog-cloud paradigm is an efficient and time-sensitive solution for remote patient surveillance. Conspicuously, a comprehensive framework based on Radio Frequency Identification Device (RFID) and body-wearable sensor technologies supported by the fog-cloud platform is proposed for the identification and management of COVID-19 patients. The J48 decision tree is used to assess the infection degree of the user based on corresponding symptoms. RFID is used to detect Temporal Proximity Interactions (TPI) among users. Using TPI quantification, Temporal Network Analysis is used to analyze and track the current stage of the COVID-19 spread. The statistical performance and accuracy of the framework are assessed by utilizing synthetically-generated data for 250,000 users. Based on the comparative analysis, the proposed framework acquired an enhanced measure of classification accuracy, and sensitivity of 96.68% and 94.65% respectively. Moreover, significant improvement has been registered for proposed fog-cloud-based data analysis in terms of Temporal Delay efficacy, Precision, and F-measure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.