AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

COVID-19

Showing 281 to 290 of 2202 articles

Clear Filters

Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.

Scientific reports
Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proa...

COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset.

Journal of virological methods
The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. In this study, we present pioneering research on COVID-19 detection using a hybrid model that combin...

Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter.

Scientific reports
The COVID-19 pandemic has disrupted people's lives and caused significant economic damage around the world, but its impact on people's mental health has not been paid due attention by the research community. According to anecdotal data, the pandemic ...

Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines.

Frontiers in immunology
With the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines also play a crucial role in cancer treatment by activating the immune system to target and destroy c...

Dual-branch Transformer for semi-supervised medical image segmentation.

Journal of applied clinical medical physics
PURPOSE: In recent years, the use of deep learning for medical image segmentation has become a popular trend, but its development also faces some challenges. Firstly, due to the specialized nature of medical data, precise annotation is time-consuming...

Comparative analysis of feature selection techniques for COVID-19 dataset.

Scientific reports
In the context of early disease detection, machine learning (ML) has emerged as a vital tool. Feature selection (FS) algorithms play a crucial role in ensuring the accuracy of predictive models by identifying the most influential variables. This stud...

Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset.

BMC infectious diseases
BACKGROUND: Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accur...

CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading.

IEEE/ACM transactions on computational biology and bioinformatics
In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervis...

Global Research on Pandemics or Epidemics and Mental Health: A Natural Language Processing Study.

Journal of epidemiology and global health
BACKGROUND: The global research on pandemics or epidemics and mental health has been growing exponentially recently, which cannot be integrated through traditional systematic review. Our study aims to systematically synthesize the evidence using natu...

Language discrepancies in the performance of generative artificial intelligence models: an examination of infectious disease queries in English and Arabic.

BMC infectious diseases
BACKGROUND: Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for...