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Pandemics

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Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?

BMC medical research methodology
BACKGROUND: The health crisis resulting from the global COVID-19 pandemic highlighted more than ever the need for rapid, reliable and safe methods of diagnosis and monitoring of respiratory diseases. To study pulmonary involvement in detail, one of t...

Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research community. With the ongoing COVID-19 pandemic, many efforts have been made to evaluate LUS data. A four...

AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning.

BMC bioinformatics
BACKGROUND: The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statisti...

Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models.

Computational and mathematical methods in medicine
INTRODUCTION: While the COVID-19 pandemic was waning in most parts of the world, a new wave of COVID-19 Omicron and Delta variants in Central Asia and the Middle East caused a devastating crisis and collapse of health-care systems. As the diagnostic ...

Association between loneliness and acceptance of using robots and pets as companions among older Chinese immigrants during the COVID-19 pandemic.

Australasian journal on ageing
OBJECTIVES: To examine loneliness experienced by middle-aged and older Chinese immigrants and its association with accepting technology as a companion (apps, Internet and robots) versus owning pets, when social distancing measures were implemented in...

TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model.

Computers in biology and medicine
BACKGROUND: As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious ...

Proposing Causal Sequence of Death by Neural Machine Translation in Public Health Informatics.

IEEE journal of biomedical and health informatics
Each year there are nearly 57 million deaths worldwide, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical for public health, especially during the COVID-19 pandemic, as institutions and government a...

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients.

Scientific reports
The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk fact...

Think positive: An interpretable neural network for image recognition.

Neural networks : the official journal of the International Neural Network Society
The COVID-19 pandemic is an ongoing pandemic and is placing additional burden on healthcare systems around the world. Timely and effectively detecting the virus can help to reduce the spread of the disease. Although, RT-PCR is still a gold standard f...

Using artificial intelligence to support rapid, mixed-methods analysis: Developing an automated qualitative assistant (AQUA).

Annals of family medicine
Context: Qualitative research - crucial for understanding human behavior - remains underutilized, in part due to the time and cost of annotating qualitative data (coding). Artificial intelligence (AI) has been suggested as a means to reduce those bur...