COVID-19

Application of machine learning for delirium prediction and analysis of associated factors in hospitalized COVID-19 patients: A comparative study using the Korean Multidisciplinary cohort for delirium prevention (KoMCoDe).

BACKGROUND: The incidence of delirium in hospitalized coronavirus disease 2019 (COVID-19) patients is linked to adverse health outcomes. Predicting the occurrence and risk factors of delirium is key to preventing its sudden onset.

Lightweight convolutional neural network for chest X-ray images classification.

In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a redesigned feature extraction (FE) module and multiscale feature (MF) module and validated using publicly available COVID-19 datasets. Experiments were conducted on multiple updated versions of the COVID-19 Radiography Database, a publicly accessibl...

Face mask identification with enhanced cuckoo optimization and deep learning-based faster regional neural network.

A mask identification and social distance monitoring system using Unmanned Aerial Vehicles (UAV) in the outdoors has been proposed for a health establ...

Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.

Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significa...

Integrating Interpretability in Machine Learning and Deep Neural Networks: A Novel Approach to Feature Importance and Outlier Detection in COVID-19 Symptomatology and Vaccine Efficacy.

In this study, we introduce a novel approach that integrates interpretability techniques from both traditional machine learning (ML) and deep neural n...

Automatic classification of HEp-2 specimens by explainable deep learning and Jensen-Shannon reliability index.

The Anti-Nuclear Antibodies (ANA) test using Human Epithelial type 2 (HEp-2) cells in the Indirect Immuno-Fluorescence (IIF) assay protocol is conside...

Using supervised machine learning algorithms to predict bovine leukemia virus seropositivity in dairy cattle in Florida: A 10-year retrospective study.

Supervised machine-learning (SML) algorithms are potentially powerful tools that may be used for screening cows for infectious diseases such as bovine...

Machine Learning Models for Predicting Monoclonal Antibody Biophysical Properties from Molecular Dynamics Simulations and Deep Learning-Based Surface Descriptors.

Monoclonal antibodies (mAbs) have found extensive applications and development in treating various diseases. From the pharmaceutical industry's perspe...

-targeted AI-driven vaccines: a paradigm shift in gastric cancer prevention.

, a globally prevalent pathogen Group I carcinogen, presents a formidable challenge in gastric cancer prevention due to its increasing antimicrobial r...

Machine learning predicts pulmonary Long Covid sequelae using clinical data.

Long COVID is a multi-systemic disease characterized by the persistence or occurrence of many symptoms that in many cases affect the pulmonary system....

Brain CT image classification based on mask RCNN and attention mechanism.

Along with the computer application technology progress, machine learning, and block-chain techniques have been applied comprehensively in various fie...

Social Robots and Sensors for Enhanced Aging at Home: Mixed Methods Study With a Focus on Mobility and Socioeconomic Factors.

BACKGROUND: Population aging affects society, with a profound impact on daily activities for those of a low socioeconomic status and with motor impair...

ParaAntiProt provides paratope prediction using antibody and protein language models.

Efficiently predicting the paratope holds immense potential for enhancing antibody design, treating cancers and other serious diseases, and advancing ...

Artificial intelligence-powered image analysis: A paradigm shift in infectious disease detection.

The global burden of infectious diseases significantly affects mortality rates, with their varying symptoms making it challenging to assess and determ...

Antigen-independent single-cell circulating tumor cell detection using deep-learning-assisted biolasers.

Circulating tumor cells (CTCs) in the bloodstream are important biomarkers for clinical prognosis of cancers. Current CTC identification methods are b...

Spatiotemporal variations of PM and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning.

Ozone pollution was widely reported along with PM reduction since 2013 in China. However, the meteorological drivers for ozone varying with different ...

Using Machine Learning Models to Predict Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

PURPOSE: Neoadjuvant chemotherapy (NAC) is increasingly used in breast cancer. Predictive modeling is useful in predicting pathologic complete respons...

Analysis of the relationships between interferon-stimulated genes and anti-SSA/Ro 60 antibodies in primary Sjögren's syndrome patients via multiomics and machine learning methods.

BACKGROUND: Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease characterized by lymphocyte infiltration of the exocrine glands....

A Multi-task learning U-Net model for end-to-end HEp-2 cell image analysis.

Antinuclear Antibody (ANA) testing is pivotal to help diagnose patients with a suspected autoimmune disease. The Indirect Immunofluorescence (IIF) mic...

Machine learning identifies cytokine signatures of disease severity and autoantibody profiles in systemic lupus erythematosus - a pilot study.

Disrupted cytokine networks and autoantibodies play an important role in the pathogenesis of systemic lupus erythematosus. However, conflicting report...