Infectious Disease

COVID-19

Latest AI and machine learning research in covid-19 for healthcare professionals.

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Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties.

Prediction of severe disease (SVD) in patients with coronavirus disease (COVID-19) pneumonia at an e...

MWTP: A heterogeneous multiplex representation learning framework for link prediction of weak ties.

Weak ties that bridge different communities are crucial for preserving global connectivity, enhancin...

Detection of β-Thalassemia trait from a heterogeneous population with red cell indices and parameters.

BACKGROUND: India is home to about 42 million people with β-thalassemia trait (βTT) necessitating sc...

Preoperative submaximal cardiopulmonary exercise testing and its association with early postoperative complications.

BACKGROUND: Early postoperative complication risk prediction would enhance perioperative surveillanc...

An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.

INTRODUCTION: Machine learning models have been employed to predict COVID-19 infections and mortalit...

Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer.

BACKGROUND: Pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) is a critic...

Reconstruction-based approach for chest X-ray image segmentation and enhanced multi-label chest disease classification.

U-Net is a commonly used model for medical image segmentation. However, when applied to chest X-ray ...

Harnessing genotype and phenotype data for population-scale variant classification using large language models and bayesian inference.

Variants of Uncertain Significance (VUS) in genetic testing for hereditary diseases burden patients ...

Testing the utility of GPT for title and abstract screening in environmental systematic evidence synthesis.

In this paper we show that OpenAI's Large Language Model (LLM) GPT perform remarkably well when used...

Improved Pine Wood Nematode Disease Diagnosis System Based on Deep Learning.

Pine wilt disease caused by the pine wood nematode, , has profound implications for global forestry ...

Comments on "Dialogue between algorithms and soil: Machine learning unravels the mystery of phthalates pollution in soil" by Pan et al. (2025).

Pan et al. demonstrated the superior predictive performance of their machine learning ML models for ...

Artificial intelligence prediction of carcinoembryonic antigen structure and interactions relevant for colorectal cancer.

Carcinoembryonic antigen (CEA) is used as a biomarker for colorectal cancer. It is expressed during ...

Combining Ultrasound Imaging and Molecular Testing in a Multimodal Deep Learning Model for Risk Stratification of Indeterminate Thyroid Nodules.

Indeterminate cytology (Bethesda III and IV) represents 15-30% of biopsied thyroid nodules and requ...

A random forest-based predictive model for classifying BRCA1 missense variants: a novel approach for evaluating the missense mutations effect.

The right classification of variants is the key to pre-symptomatic detection of disease and conducti...

Comprehensive evaluation and application of tissue clearing techniques for 3-D visualization of splenic neural and immune architecture.

As the largest secondary lymphoid organ, the spleen plays a crucial role in initiating and sustainin...

Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing.

Artificial intelligence (AI) is reshaping preclinical drug research offering innovative alternatives...

Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.

Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing gene...

Deep learning-based automatic segmentation of cerebral infarcts on diffusion MRI.

We explored effects of (1) training with various sample sizes of multi-site vs. single-site training...

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