ArticleThe Journal of supercomputing2022
Hybrid-based framework for COVID-19 prediction via federated machine learning models.
Article in The Journal of supercomputing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care.BMC health services research · 2023Pooled it
- Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.Health care management science · 2026Article
- The Value of Smartwatches in the Health Care Sector for Monitoring, Nudging, and Predicting: Viewpoint on 25 Years of Research.Journal of medical Internet research · 2024Review
- A privacy-preserving approach for cloud-based protein fold recognition.Patterns (New York, N.Y.) · 2024Article
- An AI healthcare ecosystem framework for Covid-19 detection and forecasting using CronaSona.Medical & biological engineering & computing · 2024Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- PPChain: A Blockchain for Pandemic Prevention and Control Assisted by Federated Learning.Bioengineering (Basel, Switzerland) · 2023Article
- A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges.Wireless personal communications · 2023Review
- A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.PloS one · 2023Article
- Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia.Healthcare (Basel, Switzerland) · 2022Article
- Software system to predict the infection in COVID-19 patients using deep learning and web of things.Software: practice & experience · 2022Article
- Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic.Journal of imaging · 2022Review
- A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.Frontiers in artificial intelligence · 2022Review
- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python's libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and
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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.