Latest AI and machine learning research in sepsis for healthcare professionals.
Background: Recurrent urinary tract infections(rUTI) represent a major clinical challenge due to persistent clinical symptoms, repeated antibiotic exposure, and increased risk of multidrug resistance. Further clinical management of rUTI remains challenging, as existing diagnostic and treatment guidelines are largely designed for uncomplicated, acute infections. Though uropathogenic Escherichia col...
Hospital antimicrobial resistance (AMR) emanates from an array of complex interactions between patient turnover, heterogeneous patient--staff contact patterns, antibiotic-driven within-host selection, and imperfect surveillance. We present a hospital AMR digital twin that combines mechanistic simulation with temporal graph learning to forecast resistance emergence from evolving daily contact netwo...
Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by sys...
Antimicrobial stewardship (AMS) is critical in pediatric intensive care units (PICUs), where diagnostic uncertainty often drives broad-spectrum antibi...
The rise of antibiotic-resistant bacterial infections has driven renewed interest in bacteriophage therapy, where viruses that specifically kill bacte...
In reinforcement learning, an agent learns to map representations of the environment state to predictions of future reward. Most prior work in neurosc...
D1-type receptor (D1R)-mediated dopaminergic signaling within the nucleus accumbens shell (NAcSh) is essential for forming adaptive circuit changes th...
Haemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening hyperinflammatory syndrome characterised by uncontrolled immune activation. Redu...
Decision-making circuits are modulated across life stages (e.g. juvenile, adolescent, or adult), as well as on the shorter timescale of reproductive c...
Background: Large language models (LLMs) are increasingly used in telehealth, but their safety in antibiotic prescribing remains uncertain, particular...
Introduction: Infectious and wound-healing complications after colorectal surgery often increase the complexity of local care and the need for special...
Sepsis management in the ICU requires sequential treatment decisions under rapidly evolving patient physiology. Although large language models (LLMs) ...
Reconstructing precise clinical timelines is essential for modeling patient trajectories and forecasting risk in complex, heterogeneous conditions lik...
Disease screening is critical for early detection and timely intervention in clinical practice. However, most current screening models for medical ima...
Transmissible hospital-acquired infections (HAIs) arise from complex, time-varying interactions among patients, healthcare workers, and clinical envir...
Translating transcriptomic data into therapeutic hypotheses remains fragmented and labor-intensive. Here we present ConvergeCELL, a platform combining...
Predicting transitions between health, disease, and death across biological systems remains an important challenge with significant implications for b...
Calibration of closed-loop lumped-parameter cardiovascular models remains a major bottleneck for scalable digital-twin generation because inverse esti...
Objectives This study aimed to develop and validate machine learning models to predict in-hospital mortality among systemic lupus erythematosus (SLE) ...
Excessive alcohol consumption remains a major public health challenge with limited therapeutic options. Both glucagon-like peptide-1 (GLP-1) and fibro...