Infectious Disease

Latest AI and machine learning research in infectious disease for healthcare professionals.

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A Review of In Silico Approaches for Discovering Natural Viral Protein Inhibitors in Aquaculture Disease Control.

Viral diseases pose a significant threat to the sustainability of global aquaculture, causing econom...

Spatio-temporal epidemic forecasting using mobility data with LSTM networks and attention mechanism.

The outbreak of infectious diseases can have profound impacts on socio-economic balances globally. A...

A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumor worldwide with high morbidity and mort...

Urban change detection: assessing biophysical drivers using machine learning and Google Earth Engine.

Urban areas are experiencing rapid transformations, driven by population growth, economic developmen...

Performance evaluation of reduced complexity deep neural networks.

Deep Neural Networks (DNN) have achieved state-of-the-art performance in medical image classificatio...

Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM.

BACKGROUND: Chronic hepatitis B (CHB) is a common cause of liver cirrhosis (LC), a condition associa...

Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection.

Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C ...

Comparing large language models for antibiotic prescribing in different clinical scenarios: which performs better?

OBJECTIVES: Large language models (LLMs) show promise in clinical decision-making, but comparative e...

Predicting coronavirus disease 2019 severity using explainable artificial intelligence techniques.

Predictive models for determining coronavirus disease 2019 (COVID-19) severity have been established...

Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.

Deep learning techniques are increasingly utilized to analyze large-scale single-cell RNA sequencing...

Reevaluating feature importance in machine learning: concerns regarding SHAP interpretations in the context of the EU artificial intelligence act.

This paper critically examines the analysis conducted by Maußner et al. on AI analysis, particularly...

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes.

Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the...

Deep learning by Vision Transformer to classify bacterial and fungal keratitis using different types of anterior segment images.

PURPOSE: To develop three novel Vision Transformer (ViT) frameworks for the specific diagnosis of ba...

Revolutionizing biological digital twins: Integrating internet of bio-nano things, convolutional neural networks, and federated learning.

Digital twins (DTs) are advancing biotechnology by providing digital models for drug discovery, digi...

Enhancing Relation Extraction for COVID-19 Vaccine Shot-Adverse Event Associations with Large Language Models.

OBJECTIVE: The rapid evolution of the COVID-19 virus has led to the development of different vaccine...

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