AIMC Topic:
SARS-CoV-2

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Feature Imitating Networks Enhance the Performance, Reliability and Speed of Deep Learning on Biomedical Image Processing Tasks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Feature-Imitating-Networks (FINs) are neural networks that are first trained to approximate closed-form statistical features (e.g. Entropy), and then embedded into other networks to enhance their performance. In this work, we perform the first evalua...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Tomography, X-Ray Computed Reproducibility of Results SARS-CoV-2 Brain Neoplasms Deep Learning Neural Networks, Computer COVID-19 Algorithms Humans
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Time-varying compartmental models with neural networks for pandemic infection forecasting.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
The emergence and spread of deadly pandemics has repeatedly occurred throughout history, causing widespread infections and life loss. Forecasting the progression of pandemics is crucial for decision-makers to achieve its mitigation. This predictive t...
Neural Networks, Computer Machine Learning Pandemics Humans Time Factors COVID-19 Forecasting SARS-CoV-2
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Automatic COVID-19 Detection from Chest X-ray using Deep MobileNet Convolutional Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
As the COVID-19 pandemic has put a strain on healthcare systems around the world, accurate and rapid virus detection has become increasingly important. Lung issues caused by COVID-19 can be detected using a chest X-ray (CXR). In order to automaticall...
Humans Neural Networks, Computer Support Vector Machine Lung Radiography, Thoracic COVID-19 Deep Learning Algorithms Convolutional Neural Networks SARS-CoV-2
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Cough Classification of Unknown Emerging Respiratory Disease with Federated Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Artificial intelligence offers great potential to address the need for rapid diagnostic testing in pandemic scenarios. Concerns about security and privacy, however, complicate the collection of large representative medical data. Federated Learning (F...
Cough Federated Learning COVID-19 Humans Algorithms Artificial Intelligence SARS-CoV-2 Machine Learning Pandemics
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Cough Sound Based Deep Learning Models for Diagnosis of COVID-19 Using Statistical Features and Time-Frequency Spectrum.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
This paper presents a deep learning model that can classify COVID-19 patients through cough sounds. The cough sound data were selected from the Cambridge data set which is a crowedsourced data set collected from the Cambridge COVID-19 sounds applicat...
Humans ROC Curve Cough Sound Spectrography COVID-19 SARS-CoV-2 Deep Learning
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Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases.

Mathematical biosciences and engineering : MBE Jun 3, 2024
COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbreak in 2019. Predicting infectious disease outbreaks is crucial for effective prevention and control....
Algorithms SARS-CoV-2 Disease Outbreaks Support Vector Machine Machine Learning Pandemics COVID-19 Humans
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Transforming Public Health Practice With Generative Artificial Intelligence.

Health affairs (Project Hope) Jun 1, 2024
Public health practice appears poised to undergo a transformative shift as a result of the latest advancements in artificial intelligence (AI). These changes will usher in a new era of public health, charged with responding to deficiencies identified...
Humans Artificial Intelligence Pandemics Public Health COVID-19 United States SARS-CoV-2 Public Health Practice
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Teleneurology and Artificial Intelligence in Clinical Practice.

Continuum (Minneapolis, Minn.) Jun 1, 2024
As teleheath becomes integrated into the practice of medicine, it is important to understand the benefits, limitations, and variety of applications. Telestroke was an early example of teleneurology that arose from a need for urgent access to neurolog...
Humans Neurology Artificial Intelligence COVID-19 SARS-CoV-2 Stroke Telemedicine
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Mutation prediction in the SARS-CoV-2 genome using attention-based neural machine translation.

Mathematical biosciences and engineering : MBE May 20, 2024
Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) has been evolving rapidly after causing havoc worldwide in 2020. Since then, it has been very hard to contain the virus owing to its frequently mutating nature. Changes in its genome lead t...
Mutation COVID-19 Humans Genome, Viral Algorithms Neural Networks, Computer Viral Nonstructural Proteins SARS-CoV-2
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Machine learning to understand risks for severe COVID-19 outcomes: a retrospective cohort study of immune-mediated inflammatory diseases, immunomodulatory medications, and comorbidities in a large US health-care system.

The Lancet. Digital health May 1, 2024
BACKGROUND: In the context of immune-mediated inflammatory diseases (IMIDs), COVID-19 outcomes are incompletely understood and vary considerably depending on the patient population studied. We aimed to analyse severe COVID-19 outcomes and to investig...
Risk Factors Humans Hospitalization Machine Learning Aged COVID-19 Immunomodulating Agents Retrospective Studies Female Adult SARS-CoV-2 Male Comorbidity Middle Aged COVID-19 Vaccines United States
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