Infectious Disease

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

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Interpretable Multimodal Fusion Model for Bridged Histology and Genomics Survival Prediction in Pan-Cancer.

Understanding the prognosis of cancer patients is crucial for enabling precise diagnosis and treatme...

Classification of Mycena and Species Using Deep Learning Models: An Ecological and Taxonomic Approach.

Fungi play a critical role in ecosystems, contributing to biodiversity and providing economic and bi...

DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.

BACKGROUND: Antibiotics are essential for medical procedures, food security, and public health. Howe...

Skin lesion segmentation with a multiscale input fusion U-Net incorporating Res2-SE and pyramid dilated convolution.

Skin lesion segmentation is crucial for identifying and diagnosing skin diseases. Accurate segmentat...

Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.

BACKGROUND: Tuberculosis (TB) remains a significant health concern, contributing to the highest mort...

Systematic review on visual aid technologies for surgical assistant robotic devices.

This review comprehensively analyzes the modern literature on including visual aids in diverse surgi...

Tuberculosis of the central nervous system: current concepts in diagnosis and treatment.

PURPOSE OF REVIEW: The outcome of central nervous system (CNS) tuberculosis has shown little improve...

Machine Learning-Assisted Prediction and Generation of Antimicrobial Peptides.

Antimicrobial peptides (AMPs) offer a highly potent alternative solution due to their broad-spectrum...

Machine learning models accurately predict clades of proteocephalidean tapeworms (Onchoproteocephalidea) based on host and biogeographical data.

Proteocephalids are a cosmopolitan and diverse group of tapeworms (Cestoda) that have colonized vert...

Construction and validation of a predictive model for meningoencephalitis in pediatric scrub typhus based on machine learning algorithms.

To retrospectively analyze the clinical characteristics of pediatric scrub typhus (ST) with meningoe...

Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.

Ground-glass opacities (GGOs) are hazy opacities on chest computed tomography (CT) scans that can in...

Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.

Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-...

Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.

BACKGROUND: While the COVID-19 pandemic has induced massive discussion of available medications on s...

Automated classification of chest X-rays: a deep learning approach with attention mechanisms.

BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that...

An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis.

BACKGROUND: Antimicrobial resistances pose significant challenges in health care systems. Clinical d...

CT Differentiation and Prognostic Modeling in COVID-19 and Influenza A Pneumonia.

RATIONALE AND OBJECTIVES: This study aimed to compare CT features of COVID-19 and Influenza A pneumo...

Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates.

BACKGROUND: Early and accurate confirmation of critically ill neonates with a suspected diagnosis of...

A dataset for machine learning-based QSAR models establishment to screen beta-lactamase inhibitors using the FARM -BIOMOL chemical library.

OBJECTIVES: Beta-lactamase is a bacterial enzyme that deactivates beta-lactam antibiotics, and it is...

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