AIMC Topic: Sepsis

Clear Filters Showing 51 to 60 of 397 articles

Machine learning-based text mining for cutaneous myiasis and potential value of an accidental maggot therapy for complicated skin and soft tissue infection with sepsis.

Frontiers in cellular and infection microbiology
BACKGROUND: Cutaneous myiasis, one of the most frequently diagnosed myiasis types, is defined as skin or soft tissue on a living host infested by dipterous larvae (maggots). However, bibliometric analysis of this disease remains sparse. Machine learn...

Identification of pyroptosis-related gene S100A12 as a potential diagnostic biomarker for sepsis through bioinformatics analysis and machine learning.

Molecular immunology
Sepsis is a non-discriminatory inflammatory reaction that can result in a diverse array of organ dysfunctions, which can be fatal. Pyroptosis is a programmed mechanism of cell death that is distinguishable from apoptosis and other forms of cellular d...

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

IEEE transactions on neural networks and learning systems
Dynamic treatment regimes (DTRs), which comprise a series of decisions taken to select adequate treatments, have attracted considerable attention in the clinical domain, especially from sepsis researchers. Existing sepsis DTR learning studies are mai...

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

Journal of medical Internet research
BACKGROUND: Persistent sepsis-associated acute kidney injury (SA-AKI) shows poor clinical outcomes and remains a therapeutic challenge for clinicians. Early identification and prediction of persistent SA-AKI are crucial.

Global research trends and collaborations in acute kidney injury (AKI) and sepsis: a bibliometric analysis (2004-2024).

Renal failure
BACKGROUND: Acute kidney injury (AKI) and sepsis are critical clinical conditions associated with high morbidity and mortality. Despite growing research interest, there remains a need for a comprehensive analysis of global research trends in this fie...

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

Scientific reports
Small Ubiquitin-like MOdifier-mediated modification (SUMOylation) is associated with sepsis; however, its molecular mechanism remains unclear. Herein, hub genes and regulatory mechanisms in sepsis was investigated. The GSE65682 and GSE95233 datasets ...

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

Shock (Augusta, Ga.)
Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (previously subphenotyped hypolipoprotein phenotype [HYPO]) or higher lipoprote...

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

BMC infectious diseases
OBJECTIVE: The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients.

Association between the (neutrophil + monocyte)/albumin ratio and all-cause mortality in sepsis patients: a retrospective cohort study and predictive model establishment according to machine learning.

BMC infectious diseases
INTRODUCTION: Sepsis is a life-threatening condition characterized by widespread inflammatory response syndrome in the body resulting from infection. Previous studies have demonstrated that some inflammatory factors or nutritional elements contribute...

A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study.

BMC infectious diseases
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication associated with a high mortality rate in patients with sepsis. Early identification of patients with sepsis at high risk of developing ARDS is crucial for timely interven...