Latest AI and machine learning research in sepsis for healthcare professionals.
Fluorescence intravital microscopy captures large data sets of dynamic multicellular interactions wi...
Fusarium head blight (FHB) is a plant disease caused by various species of the fungus. One of the m...
Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction ...
Seneca Valley virus (SVV) and foot-and-mouth disease virus (FMDV) belong to the Picornaviridae famil...
The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Ac...
Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among c...
BACKGROUND: There is a growing demand for advanced methods to improve the understanding and predicti...
BACKGROUND: Invasive Escherichia coli disease (IED), also known as invasive extraintestinal pathogen...
With the rapid development of information technology, great changes have taken place in the way of m...
BACKGROUND: Nosocomial infections (NIs) frequently occur and adversely impact prognosis for hospital...
Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to di...
Early and accurate detection of colorectal cancer (CRC) is critical for improving patient outcomes. ...
The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In respon...
BACKGROUND: With the development of artificial intelligence, the application of machine learning to ...
BACKGROUND: Exposure to air pollution is a major health problem worldwide. This study aimed to inves...
, a notable drug-resistant bacterium, often induces severe infections in healthcare settings, prompt...
HPV is a ubiquitous pathogen implicated in cervical and other cancers. Although vaccines are availab...
BACKGROUND: A bloodstream infection (BSI) prognostic score applicable at the time of blood culture c...
BACKGROUND: Timely diagnosis of neonatal sepsis is challenging. We aimed to systematically evaluate ...
INTRODUCTION: Various Machine Learning (ML) models have been used to predict sepsis-associated morta...
BACKGROUND: Current diagnostic methods cannot effectively distinguish between latent tuberculosis in...