Infectious Disease

Assessment and Prediction of Clinical Outcomes for ICU-Admitted Patients Diagnosed with Hepatitis: Integrating Sociodemographic and Comorbidity Data

Hepatitis, a disease characterized by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million deaths annually, with hepatitis B and C accounting for many of these fatalities. Intensive care unit (ICU) management of patients is particularly challenging due to the complex clinical care and resource demands. This study focuses on predicting length of stay ...

Multi-model approach to understand and predict past and future dengue epidemic dynamics

Understanding the past, current, and future dynamics of dengue epidemics is challenging yet increasingly important for global public health. Using data from northern Peru across 2010 – 2021, we introduce a multi-model approach that integrates new and existing techniques for understanding and predicting dengue epidemics. Using wavelet analyses, we unveil spatiotemporal patterns and estimate space-v...

A Hybrid AutoML Ensemble Integrating Conventional Learners and Gradient-Boosting Models for Multi-Outcome Prediction in ICU Patients with Pseudomonas aeruginosa

Carbapenem resistance in Pseudomonas aeruginosa is increasing in intensive care units (ICUs). To enhance antimicrobial stewardship and infection contr...

Leveraging Machine Learning for Developing and Validating a Neonatal Acute Kidney Injury Prediction Model (NEPHRO): A Comprehensive Evidence-Based Neonatal AKI Risk Stratification Tool

Acute kidney injury (AKI) is a serious and common complication among critically ill neonates. Preventing or treating AKI early requires timely predict...

CLIF-Net: Intersection-guided Cross-view Fusion Network for Infection Detection from Cranial Ultrasound

This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bac...

Case-Control Matching Erodes Feature Discriminability for AI-driven Sepsis Prediction in ICUs: A Retrospective Cohort Study

Sepsis remains a leading cause of intensive care unit (ICU) mortality worldwide, and early detection is essential for improving survival through timel...

Omics Integration Uncovers Mechanisms Associated with HIV Viral Load and Potential Therapeutic Insights

While antiretroviral therapy (ART) has significantly improved disease prognosis in people with HIV (PWH), understanding the biological mechanisms unde...

Development of a pilot machine learning model to predict successful cure in critically ill patients with community-acquired pneumonia

Severe community-acquired pneumonia (CAP) remains a major cause of critical illness, yet there are no validated early clinical criteria to predict sho...

Efficient Chest X-Ray Feature Extraction and Feature Fusion for Pneumonia Detection Using Lightweight Pretrained Deep Learning Models

Pneumonia is a respiratory condition characterized by inflammation of the alveolar sacs in the lungs, which disrupts normal oxygen exchange. This dise...

Direct causal variable discovery leveraging the invariance principle: application in biomedical studies

Accurate identification of direct causal (parental) variables for a target is of primary interest in many applications, especially in biomedical scien...

Advances and limitations of artificial intelligence-assisted identification of pathogenic fungi

We developed and tested multiple computer-vision image classifiers, for their ability to identify a large set of common and rare pathogenic molds. Aim...

Secure and Efficient Federated Learning for Predictive Modeling in Resource-Constrained Healthcare Systems

Predictive modeling in healthcare holds promise for improving clinical outcomes, but in many low-resource settings, data fragmentation, privacy concer...

Automating Handwritten Vaccination Record Transcription with Generative Multimodal AI Models: A Proof of Concept Study from The Gambia

Handwritten home-based vaccination records (HBRs) are a vital source of immunization data, yet manual transcription in household surveys is time-consu...

Stochastic Chaos in Influenza Data – An Application of Topological Methods

Empirical methods of chaos theory have been applied to epidemiological data, uncovering evidence of chaos. In the current work, we apply, to the weekl...

Characteristics and Early Diagnosis of Motor Neuron Disease (MND) in 67 million individuals in England: a comparative study on phenotyping models derived by AI, Knowledge Graphs and the MND Association

Motor neuron disease (MND) is a rapidly progressive and fatal neurodegenerative condition, making early diagnosis critical for optimizing patient outc...

A conversational artificial intelligence based web application for medical conversations: a prototype for a chatbot

Artificial Intelligence (AI) has evolved through various trends, with different subfields gaining prominence over time. Currently, Conversational Arti...

Associations Between Meteorological Factors and Influenza A/B Incidence in Subtropical China: A Six-Year Surveillance Study with Deep Learning Modelling for Influenza Early Warning

Influenza burden in subtropical regions like southeastern China is shaped by meteorological factors-driven complex transmission patterns that differ f...

Revolutionizing Lung Cancer Detection: Evaluating AI Models for VOC Analysis and Unveiling Key Exhaled Biomarkers

Volatile Organic Compounds (VOCs) are organic chemicals that readily vaporize at room temperature and are emitted from diverse sources, including pain...

Incidence of Long COVID Following Reinfection with COVID-19

COVID-19 reinfections have emerged as a critical concern, particularly in relation to post-acute sequelae of SARS-CoV-2 infection, commonly known as l...

Development and Validation of VC-MAES and VC-SEPS: Deep Learning-Based Early Warning Systems for Hospitalized Patients

The timely detection of ward deterioration—including unplanned intensive care unit (ICU) transfer, cardiac arrest, death, and sepsis—remains an unmet ...