Latest AI and machine learning research in sepsis for healthcare professionals.
Capturing the structured mixing within a population is key to the reliable projection of infectious ...
Early identification of patients at risk for clinical deterioration in the intensive care unit (ICU)...
Antibiotic development is challenged by high costs and failure rates. Artificial intelligence (AI) h...
Antimicrobial resistance (AMR) poses a global health threat, reducing the effectiveness of antibioti...
Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces ...
Rationale Autonomic dysfunction is a hallmark of sepsis pathophysiology, yet its quantification rema...
Background: The 2024 blood culture bottle shortage brought diagnostic resource allocation to the for...
Trypanosoma cruzi infection remains a central, yet methodologically challenging step in Chagas disea...
Background: Sepsis remains a leading cause of preventable hospital mortality in England, with NHS En...
The rise of Antimicrobial Resistance, particularly Multi-Drug Resistance (MDR), presents a critical ...
Tuberculosis (TB) is the worldwide leading infectious killer due to a single pathogen and increasing...
Fecal microbiota transplantation (FMT) has emerged as a highly effective treatment for recurrent Clo...
Irregular multivariate time series with missing values present significant challenges for predictive...
Neuromodulatory systems, notably basal forebrain cholinergic and midbrain dopaminergic pathways, cri...
Activity of the mesolimbic system is essential for adaptive performance of reward-related behaviors....
Assessing the human infection potential of emerging coronaviruses remains a critical challenge for g...
Safe and interpretable sequential decision-making is critical in healthcare, yet reinforcement learn...
Norepinephrine (NE) is an important mediator of sympathetic activity that influences breathing. At t...
Accurately characterising mosquito infection dynamics is essential for effective dengue prevention a...
Background: Inadequate data in electronic health records can create problems for clinical decision s...
Offline Reinforcement Learning (RL) promises the recovery of optimal policies from static datasets, ...