Infectious Disease

A novel classical machine learning framework for early sepsis prediction using electronic health record data from ICU patients.

Sepsis, a life-threatening condition triggered by the body's response to infection, remains a significant global health challenge, annually affecting millions in the United States alone with substantial mortality and healthcare costs. Early prediction of sepsis is critical for timely intervention and improved patient outcomes. This study introduces an innovative predictive model leveraging machine...

The Transformative Impact of AI, Extended Reality, and Robotics in Interventional Radiology: Current Trends and Applications.

Interventional Radiology is at the forefront of integrating advanced imaging techniques and minimally-invasive procedures to enhance patient care. The advent of Digital Health Technologies (DHTs), including artificial intelligence (AI), robotics, and extended reality (XR), is revolutionizing healthcare, particularly in IR due to its reliance on innovative technology and advanced imaging. Since 201...

Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning-Based Predictive Model.

BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a serious postoperative complication among older adult surgical patients that frequently...

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes.

Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, represents a continuum that may escalate from mild s...

Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation.

Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by...

Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding.

BACKGROUND: The global outbreak of the coronavirus disease 2019 (COVID-19) has been enormously damaging, in which prolonged shedding of severe acute r...

Machine learning-based identification of general transcriptional predictors for plant disease.

This study investigated the generalizability of Arabidopsis thaliana immune responses across diverse pathogens, including Botrytis cinerea, Sclerotini...

Preferences for attributes of an artificial intelligence-based risk assessment tool for HIV and sexually transmitted infections: a discrete choice experiment.

INTRODUCTION: Early detection and treatment of HIV and sexually transmitted infections (STIs) are crucial for effective control. We previously develop...

Modified mutual information feature selection algorithm to predict COVID-19 using clinical data.

The COVID-19 pandemic has profoundly impacted health, emphasizing the need for timely disease detection. Blood tests have become key diagnostic tools ...

Advances in diagnosis and prognosis of bacteraemia, bloodstream infection, and sepsis using machine learning: A comprehensive living literature review.

BACKGROUND: Blood-related infections are a significant concern in healthcare. They can lead to serious medical complications and even death if not pro...

Artificial intelligence meets the world experts; updates and novel therapies in autoimmunity - The 14th international congress on autoimmunity 2024 (AUTO14), Ljubljana.

The bi-annual international congress on autoimmunity is a huge opportunity for the medical community to discuss the latest updates in the field. Durin...

Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.

The ability to predict HIV-1 resistance to broadly neutralizing antibodies (bnAbs) will increase bnAb therapeutic benefits. Machine learning is a powe...

Discovery of Active Ingredient of Yinchenhao Decoction Targeting TLR4 for Hepatic Inflammatory Diseases Based on Deep Learning Approach.

Yinchenhao Decoction (YCHD), a classic formula in traditional Chinese medicine, is believed to have the potential to treat liver diseases by modulatin...

Antimicrobial efficacy of an experimental UV-C robot in controlled conditions and in a real hospital scenario.

BACKGROUND: Among no-touch automated disinfection devices, ultraviolet-C (UV-C) radiation has been proven to be one of the most effective against a br...

Performance of deep learning models in predicting the nugent score to diagnose bacterial vaginosis.

The Nugent score is a commonly used diagnostic tool for bacterial vaginosis. However, its accuracy depends on the skills of laboratory technicians. Th...

Comprehensive review of dysphagia and technological advances in dysphagia food.

As the global population ages, dysphagia is becoming increasingly common among the elderly, posing serious risks such as choking, aspiration pneumonia...

Convolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer.

Early Diagnosis of oral cancer is very important and can save you from some oral malignancies. However, while this approach aids in the rapid healing ...

Analysis and prediction of infectious diseases based on spatial visualization and machine learning.

Infectious diseases are a global public health problem that poses a threat to human society. Since the 1970s, constantly mutated new infectious viruse...

Interpretation of Bacterial Smears and Cultures Using Artificial Intelligence.

The use of artificial intelligence (AI) to aid in the diagnosis of infectious diseases is a growing area of interest. AI-based applications in the cli...

Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm.

This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on pre...