Infectious Disease

Machine Learning-Based Prediction of In-Hospital Mortality in Severe COVID-19 Patients Using Hematological Markers.

The mortality rate is very high in patients with severe COVID-19. Nearly 32% of COVID-19 patients are critically ill, with mortality rates ranging from 8.1% to 33%. Early risk factor detection makes it easier to get the right care and estimate the prognosis. This study aimed to develop and validate a model to predict the risk of mortality based on hematological parameters at hospital admission in...

Multimodal deep learning model for prediction of prognosis in central nervous system inflammation.

Inflammatory diseases of the CNS impose a substantial disease burden, necessitating prompt and appropriate prognosis prediction. We developed a multimodal deep learning model integrating clinical features and brain MRI data to enhance early prognosis prediction of CNS inflammation. This retrospective study used thin-cut T1-weighted brain MRI scans and the clinical variables of patients with CNS in...

The dynamic linkage between covid-19 and nutrition: a review from a probiotics perspective using machine learning and bibliometric analysis.

INTRODUCTION: The pandemic crisis is now a memorable milestone in the history of science, not only for the impacts on the population's health but also...

Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies.

Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. R...

Surface-Enhanced Raman Scattering Nanotags: Design Strategies, Biomedical Applications, and Integration of Machine Learning.

Surface-enhanced Raman scattering (SERS) is a transformative technique for molecular identification, offering exceptional sensitivity, signal specific...

Artificial intelligence in vaccine research and development: an umbrella review.

BACKGROUND: The rapid development of COVID-19 vaccines highlighted the transformative potential of artificial intelligence (AI) in modern vaccinology,...

Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model.

BACKGROUND: Tuberculous pleural effusion (TPE) is a prevalent tuberculosis complication, with diagnosis presenting considerable challenges. Timely and...

IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective.

COVID-19 has not only changed the way people live but has also altered the way all organizations operate. The most effective precautionary measure aga...

A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information.

Understanding the behavior of microbial consortia is crucial for predicting metabolite production by microorganisms. Genome-scale network reconstructi...

Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach.

BACKGROUND: Evidence suggests a bidirectional association between chronic obstructive pulmonary disease (COPD) and sepsis, but the underlying mechanis...

AI-driven multi-omics profiling of sepsis immunity in the digestive system.

Sepsis is a life-threatening systemic inflammatory syndrome characterized by a complex immune biphasic imbalance. Monitoring of immune status has not ...

AI in fungal drug development: opportunities, challenges, and future outlook.

The application of artificial intelligence (AI) in fungal drug development offers innovative strategies to address the escalating threat of fungal inf...

Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis.

INTRODUCTION: Tuberculous meningitis (TBM) leads to high mortality, especially amongst individuals with HIV. Predicting the incidence of disease-relat...

Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.

BACKGROUND: Post-COVID conditions (PCC) have proven difficult to diagnose. In this retrospective observational study, we aimed to characterize the lev...

Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.

Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperati...

A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2.

BACKGROUND: Patient symptoms, crucial for disease progression and diagnosis, are often captured in unstructured clinical notes. Large language models ...

Waist rotation angle as indicator of probable human collision-avoidance direction for autonomous mobile robots.

The likelihood of pedestrians encountering autonomous mobile robots (AMRs) in smart cities is steadily increasing. While previous studies have explore...

Segmentation-based lightweight multi-class classification model for crop disease detection, classification, and severity assessment using DCNN.

Leaf diseases in Zea mays crops have a significant impact on both the calibre and volume of maize yield, eventually impacting the market. Prior detect...

Modeling protective meningococcal antibody responses and factors influencing antibody persistence following vaccination with MenAfriVac using machine learning.

Meningococcal meningitis poses a significant public health burden in the meningitis belt region of sub-Saharan Africa. The introduction of the meningo...

EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.

BACKGROUND: The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe ac...