AIMC Topic: COVID-19
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A new scoring in differential diagnosis: multisystem inflammatory syndrome or adenovirus infection?
Turkish journal of medical sciences
Oct 6, 2024
BACKGROUND/AIM: Differentiating multisystem inflammatory syndrome in children (MIS-C) from adenovirus infection (AI) can be challenging due to similar clinical and laboratory findings. This study aimed to identify distinguishing characteristics and d...
Intelligent computing framework to analyze the transmission risk of COVID-19: Meyer wavelet artificial neural networks.
Computational biology and chemistry
Oct 2, 2024
The optimum control methods for the epidemiology of the COVID-19 model are acknowledged using a novel advanced intelligent computing infrastructure that joins artificial neural networks with unsupervised learning-based optimizers i.e., Genetic Algori...
Quantification of uncertainty in short-term tropospheric column density risks for a wide range of carbon monoxide.
Journal of environmental management
Oct 2, 2024
The short-term risks associated with atmospheric trace gases, particularly carbon monoxide (CO), are critical for ecological security and human health. Traditional statistical methods, which still dominate the assessment of these risks, limit the pot...
Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections.
Scientific reports
Sep 30, 2024
The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning t...
Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.
Scientific reports
Sep 30, 2024
Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based a...
Managing spatio-temporal heterogeneity of susceptibles by embedding it into an homogeneous model: A mechanistic and deep learning study.
PLoS computational biology
Sep 30, 2024
Accurate prediction of epidemics is pivotal for making well-informed decisions for the control of infectious diseases, but addressing heterogeneity in the system poses a challenge. In this study, we propose a novel modelling framework integrating the...
Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.
Frontiers in immunology
Sep 30, 2024
BACKGROUNDS: During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world's healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) ...
How generative artificial intelligence portrays science: Interviewing ChatGPT from the perspective of different audience segments.
Public understanding of science (Bristol, England)
Sep 29, 2024
Generative artificial intelligence in general and ChatGPT in particular have risen in importance. ChatGPT is widely known and used increasingly as an information source for different topics, including science. It is therefore relevant to examine how ...
Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency.
Artificial intelligence in medicine
Sep 29, 2024
BACKGROUND & OBJECTIVES: Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighted gaps in preparedness, emphasizing the need for early identification of at-risk groups and targeted...
COVID-19 from symptoms to prediction: A statistical and machine learning approach.
Computers in biology and medicine
Sep 28, 2024
During the COVID-19 pandemic, the analysis of patient data has become a cornerstone for developing effective public health strategies. This study leverages a dataset comprising over 10,000 anonymized patient records from various leading medical insti...
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