Critical Care

Sepsis

Latest AI and machine learning research in sepsis for healthcare professionals.

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Showing 1198-1218 of 7,148 articles
Identification of pyroptosis-related gene S100A12 as a potential diagnostic biomarker for sepsis through bioinformatics analysis and machine learning.

Sepsis is a non-discriminatory inflammatory reaction that can result in a diverse array of organ dys...

May 2025 40318597
ADT²R: Adaptive Decision Transformer for Dynamic Treatment Regimes in Sepsis.

Dynamic treatment regimes (DTRs), which comprise a series of decisions taken to select adequate trea...

(2)
May 2025 39208049
GBE1 alleviates MPTP-induced PD symptoms in mice by enhancing glycolysis and oxidative phosphorylation.

In Parkinson's disease (PD), the disturbance of energy metabolism due to glucose metabolic reprogram...

Apr 2025 40316161
Explainable Machine Learning Model for Predicting Persistent Sepsis-Associated Acute Kidney Injury: Development and Validation Study.

BACKGROUND: Persistent sepsis-associated acute kidney injury (SA-AKI) shows poor clinical outcomes a...

Apr 2025 40200699
Deep learning for fetal inflammatory response diagnosis in the umbilical cord.

INTRODUCTION: Inflammation of the umbilical cord can be seen as a result of ascending intrauterine i...

Apr 2025 40294507
Integrating bioinformatics and machine learning to discover sumoylation associated signatures in sepsis.

Small Ubiquitin-like MOdifier-mediated modification (SUMOylation) is associated with sepsis; however...

Apr 2025 40274894
Integrating AI for infectious disease prediction: A hybrid ANN-XGBoost model for leishmaniasis in Pakistan.

Addressing leishmaniasis infection remains a substantial challenge in KP-Pakistan due to the increas...

Apr 2025 40280350
Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data.

OBJECTIVE: The aim of this study was to construct interpretable machine learning models to predict t...

Apr 2025 40269746
Host Biomarkers and Antibiotic Tissue Penetration in Sepsis: Insights from Moxifloxacin.

BACKGROUND AND OBJECTIVE: Sepsis-induced pathophysiological changes may lead to pharmacokinetic vari...

Apr 2025 40268824
A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.

Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains a signif...

(1)
Apr 2025 40258881
Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic....

Apr 2025 40251182
Automated machine learning for early prediction of systemic inflammatory response syndrome in acute pancreatitis.

BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of...

(1)
Apr 2025 40247291
Aptamer-functionalized graphene quantum dots combined with artificial intelligence detect bacteria for urinary tract infections.

OBJECTIVES: Urinary tract infection is one of the most prevalent forms of bacterial infection, and p...

Apr 2025 40308970
Using pathology images and artificial intelligence to identify bacterial infections and their types.

Bacterial infections pose a significant biosafety concern, making early and accurate diagnosis essen...

Apr 2025 40233851
Multifaceted profiling of virus-specific CD8 T cells reveals distinct immune signatures against cytomegalovirus infection states during pregnancy.

Anti-cytomegalovirus (CMV) serological testing, including the IgG avidity index (AI), is used to ass...

Apr 2025 40343282
Micro- and Nano-Bots for Infection Control.

Medical micro- and nano-bots (MMBs and MNBs) have attracted a lot of attention owing to their precis...

Apr 2025 40211645
CLIC1 and IFITM2 expression in brain tissue correlates with cognitive impairment via immune dysregulation in sepsis and Alzheimer's disease.

BACKGROUND: Sepsis, a life-threatening condition driven by dysregulated host responses to infection,...

Apr 2025 40215772
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