AIMC Topic: Sepsis

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An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.

PloS one May 20, 2024
Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machine learning model for early prediction of 28-day mortality in patients with SALI. Data from the Medi...
Female Humans Middle Aged Machine Learning Risk Factors Prognosis Sepsis Aged Liver Diseases Male
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Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

Critical care (London, England) May 10, 2024
BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous ...
Sepsis Retrospective Studies Hospital Mortality Acute Kidney Injury Creatinine Critical Illness Intensive Care Units Humans Female Machine Learning Aged Biomarkers Male Middle Aged
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Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.

Cardiovascular diabetology May 9, 2024
BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the dis...
Sepsis Blood Glucose Time Factors Hospital Mortality Middle Aged Risk Assessment China Hyperglycemia Predictive Value of Tests Cause of Death Biomarkers Prognosis Retrospective Studies Critical Illness Female Risk Factors Aged Male Humans Machine Learning Decision Support Techniques Databases, Factual
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Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.

Journal of critical care May 8, 2024
PURPOSE: This study investigates the potential of machine learning (ML) algorithms in improving sepsis diagnosis and prediction, focusing on their relevance in healthcare decision-making. The primary objective is to contribute to healthcare decision-...
Artificial Intelligence Machine Learning Algorithms Sensitivity and Specificity Humans Sepsis Bayes Theorem
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Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

The Pediatric infectious disease journal May 8, 2024
BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sep...
Support Vector Machine Infant, Newborn Neural Networks, Computer Adolescent Artificial Intelligence Child, Preschool Male Humans Retrospective Studies Child Infant Intensive Care Units, Pediatric Female Sepsis
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Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Critical care explorations May 6, 2024
Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine...
Machine Learning Precision Medicine Sepsis Resuscitation Humans
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A Machine learning model for predicting sepsis based on an optimized assay for microbial cell-free DNA sequencing.

Clinica chimica acta; international journal of clinical chemistry May 4, 2024
OBJECTIVE: To integrate an enhanced molecular diagnostic technique to develop and validate a machine-learning model for diagnosing sepsis.
Female Aged Male Middle Aged Humans Machine Learning Prospective Studies Sepsis Sequence Analysis, DNA Cell-Free Nucleic Acids
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A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit.

PloS one May 1, 2024
Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient's outcome. Data on machine-le...
Humans Adolescent Child, Preschool Machine Learning Child Retrospective Studies Bacteremia Intensive Care Units, Pediatric Female Early Diagnosis Male Infant, Newborn Infant Sepsis
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Early prediction of sepsis-induced respiratory tract infection using a biomarker-based machine-learning algorithm.

Scandinavian journal of clinical and laboratory investigation Apr 29, 2024
Early and differential diagnosis of sepsis is essential to avoid unnecessary antibiotic use and further reduce patient morbidity and mortality. Here, we aimed to identify predictors of sepsis and advance a machine-learning strategy to predict sepsis-...
Sepsis Respiratory Tract Infections Aged Male Middle Aged Humans Algorithms Machine Learning Biomarkers Retrospective Studies Female
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Predicting community acquired bloodstream infection in infants using full blood count parameters and C-reactive protein; a machine learning study.

European journal of pediatrics Apr 18, 2024
Early recognition of bloodstream infection (BSI) in infants can be difficult, as symptoms may be non-specific, and culture can take up to 48 h. As a result, many infants receive unneeded antibiotic treatment while awaiting the culture results. In thi...
Biomarkers Retrospective Studies Female Infant, Newborn Sensitivity and Specificity Male Infant Blood Cell Count Sepsis Bacteremia Decision Trees Predictive Value of Tests Humans C-Reactive Protein Machine Learning
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