Sepsis

Development and validation of an XGBoost model with SHAP-based interpretability and a web-based calculator for predicting extrauterine growth restriction in preterm infants

Background: Extrauterine growth restriction (EUGR) is a common and clinically significant complication among preterm infants, contributing to adverse neurodevelopmental and metabolic outcomes. Early and individualized risk prediction remains challenging. This study aimed to develop and validate an interpretable machine learning model for early prediction of EUGR using routinely available clinical ...

Predicting COVID-19 incidence from seroprevalence and population-based cohort data using interpretable machine learning with differential privacy analysis

During the COVID-19 pandemic, reported incidence data played a central role in public health surveillance and in tracking epidemic dynamics, although they provide limited insight into the behavioral, immunological, and socioeconomic drivers of transmission.Population-based seroprevalence studies with linked survey data offer a rich but untapped source of individual-level information that can compl...

Predicting highly pathogenic avian influenza H5N1 outbreak risk using extreme weather and bird migration data in machine learning models

Background. Climate change is intensifying extreme weather events (EWEs) with potentially profound consequences for zoonotic disease dynamics, yet the...

Integrated Bioinformatics Analysis Identifies and Validates Novel Cellular Senescence-Associated Genes in Sepsis and Sepsis-Induced ARDS

Sepsis can lead to acute respiratory distress syndrome (ARDS) and is associated with a high mortality rate. This study investigated cellular senescenc...

Contrastive Transformer-Driven Discovery of Temporal Hemodynamic Subphenotypes in Cardiac Surgery Patients

Cardiac surgery patients experience rapidly evolving hemodynamics in early post-operative period requiring intensive support. Identifying hemodynamic ...

Inter-individual variability in lipoprotein proteomics reveals distinct patient clusters informative for disease pathogenesis and severity

Lipoprotein composition is altered in sepsis, and supplementation with high-density lipoproteins has been reported to improve outcomes in experimental...

ECG spectrogram-based deep learning model to predict deterioration of patients with early sepsis at the emergency department: a study from the Acutelines data- and biobank

Purpose: Early recognition of deterioration in patients with suspected infection at the emergency department (ED) is important. Current clinical scori...

Brain-Cognitive Gaps in relation to Dopamine and Health-related Factors: Insights from AI-Driven Functional Connectome Predictions

A key question in human neuroscience is to understand how individual differences in brain function relate to cognitive differences. However, the optim...

FoundedPBI: Using Genomic Foundation Models to predict Phage-Bacterium Interactions

The scalability of phage therapy as a viable alternative or complement to antibiotics is limited by the labor-intensive experimental screening require...

Attention-based Pin Site Image Classification in Orthopaedic Patients with External Fixators

Pin sites represent the interface where a metal pin or wire from the external environment passes through the skin into the internal environment of the...

Multi-timescale Computation by Astrocytes

The basic computational unit of the brain has long been defined as the neuron. However, mounting evidence suggests that other cells, especially astroc...

Context-Aware Emergency Department Triage Using Pairwise Comparisons and Bradley-Terry Aggregation

Objective: To evaluate a ranking approach for emergency department (ED) waiting room prioritization that uses pairwise clinical comparisons aggregated...

Rapid Bacterial Identification and Antibiotic Susceptibility Testing through Interferometry-based Surface Topography Measurement

Antimicrobial resistance poses a critical global health threat. For many bacterial infections, such as bacteremia, treatment can fail due to the time ...

Reinforcement Learning for Antibiotic Stewardship: Optimizing Prescribing Policies Under Antimicrobial Resistance Dynamics

Antimicrobial resistance (AMR) threatens antibiotic effectiveness, but quantitatively evaluating stewardship strategies under partial observability an...

A Machine Learning Framework for Constructing Heterogeneous Contact Networks: Implications for Epidemic Modelling

Capturing the structured mixing within a population is key to the reliable projection of infectious disease dynamics and hence informed control. Both ...

Multimodal Deep Learning for Early Prediction of Patient Deterioration in the ICU: Integrating Time-Series EHR Data with Clinical Notes

Early identification of patients at risk for clinical deterioration in the intensive care unit (ICU) remains a critical challenge. Delayed recognition...

Fleming: An AI Agent for Antibiotic Discovery in Mycobacterium Tuberculosis

Antibiotic development is challenged by high costs and failure rates. Artificial intelligence (AI) holds promise to overcome these challenges by predi...

abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance

Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibiotics and complicating clinical decision-making. To a...

Hospital AI and Robotics Adoption, Access Inequality, and County Mortality: A National Study Across 3,143 U.S. Counties

Hospital artificial intelligence (AI) and robotics are spreading unevenly across the United States, yet national evidence on how these technologies ar...

Optimising antibiotic switching via forecasting of patient physiology

Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare...