AIMC Topic: Sepsis

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Time-series deep learning and conformal prediction for improved sepsis diagnosis in primarily Non-ICU hospitalized patients.

Computers in biology and medicine
PURPOSE: Sepsis, a life-threatening condition from an uncontrolled immune response to infection, is a leading cause of in-hospital mortality. Early detection is crucial, yet traditional diagnostic methods, like SIRS and SOFA, often fail to identify s...

A systematic methodological evaluation of sepsis guidelines: Protocol for quality assessment and consistency of recommendations.

Acta anaesthesiologica Scandinavica
BACKGROUND: Sepsis is a leading cause of mortality worldwide, characterized by a dysregulated host response to infection. Despite the development of multiple clinical practice guidelines (CPGs) to standardize sepsis management, substantial variabilit...

Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis.

Clinical and experimental pharmacology & physiology
Sepsis is a life-threatening disease caused by the dysregulation of the immune response. It is important to identify influential genes modulating the immune response in sepsis. In this study, we used P-NET, a biologically informed explainable artific...

GLOBAL TRENDS IN ARTIFICIAL INTELLIGENCE AND SEPSIS-RELATED RESEARCH: A BIBLIOMETRIC ANALYSIS.

Shock (Augusta, Ga.)
Background: In the field of bibliometrics, although some studies have conducted literature reviews and analyses on sepsis, these studies mainly focus on specific areas or technologies, such as the relationship between the gut microbiome and sepsis, o...

MACHINE LEARNING AND BIOINFORMATICS TO IDENTIFY COAGULATION BIOMARKERS IN SEPSIS-RELATED KIDNEY INJURY.

Shock (Augusta, Ga.)
Background: Sepsis-associated acute kidney injury (SA-AKI) is a life-threatening complication with mortality rates exceeding 50%, yet its molecular drivers remain poorly defined. Dysregulated coagulation is increasingly implicated in SA-AKI pathogene...

EVALUATION OF PROGNOSTIC RISK MODELS BASED ON AGE AND COMORBIDITY IN SEPTIC PATIENTS: INSIGHTS FROM MACHINE LEARNING AND TRADITIONAL METHODS IN A LARGE-SCALE, MULTICENTER, RETROSPECTIVE STUDY.

Shock (Augusta, Ga.)
Background: Age and comorbidity significantly impact the prognosis of septic patients and inform treatment decisions. To provide clinicians with effective tools for identifying high-risk patients, this study assesses the predictive value of the age-a...

Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey.

Critical care explorations
IMPORTANCE: Sepsis is a major cause of morbidity and mortality, with early intervention shown to improve outcomes. Predictive modeling and artificial intelligence (AI) can aid in early sepsis recognition, but there remains a gap between algorithm dev...

Prognostic value of the Glucose-to-Albumin ratio in sepsis-related mortality: A retrospective ICU study.

Diabetes research and clinical practice
AIMS: To investigate the prognostic value of the glucose-to-albumin ratio (GAR) in predicting 30-day and 90-day mortality in septic ICU patients.

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

Computer methods and programs in biomedicine
OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage remains debated. This study aims to determine the optimal multi-stage fluid resuscitation dosage for sepsis patients.

Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study.

JMIR medical informatics
BACKGROUND: The development of sepsis in the intensive care unit (ICU) is rapid, the golden rescue time is short, and the effective way to reduce mortality is rapid diagnosis and early warning. Therefore, real-time prediction models play a key role i...