Critical Care

Sepsis

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

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Critical-Care Subcategories: Sepsis
Showing 190-210 of 9,027 articles
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...

The future of HIV diagnostics: an exemplar in infectious diseases.

Over the past 40 years, diagnostics have become the backbone of HIV prevention, treatment, and reten...

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...

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...

An investigation into the impact of temporality on COVID-19 infection and mortality predictions: new perspective based on Shapley Values.

INTRODUCTION: Machine learning models have been employed to predict COVID-19 infections and mortalit...

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

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

IDENTIFYING A SEPSIS SUBPHENOTYPE CHARACTERIZED BY DYSREGULATED LIPOPROTEIN METABOLISM USING A SIMPLIFIED CLINICAL DATA ALGORITHM.

Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. ...

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...

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...

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

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

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...

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....

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...

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...

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...

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...

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...

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