AIMC Topic: Sepsis

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Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator.

PloS one Jul 16, 2025
BACKGROUND: Sepsis-associated delirium (SAD) occurs due to disruptions in neurotransmission linked to inflammatory responses from infections. It poses significant challenges in clinical management and is associated with poor outcomes. Survivors often...
Algorithms Retrospective Studies Critical Illness Intensive Care Units Humans Machine Learning Middle Aged Risk Assessment Sepsis Delirium Female Risk Factors Aged Male
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Mortality and antibiotic timing in deep learning-derived surviving sepsis campaign risk groups: a multicenter study.

Critical care (London, England) Jul 14, 2025
BACKGROUND: The current Surviving Sepsis Campaign (SSC) guidelines provide recommendations on timing of administering antibiotics in sepsis patients based on probability of sepsis and presence of shock. However, there have been minimal efforts to str...
Mortality Middle Aged Risk Assessment Deep Learning Sepsis Cohort Studies Time Factors Hospital Mortality Female Aged Aged, 80 and over Male Humans Anti-Bacterial Agents Prospective Studies
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Identifying propionate metabolism-related genes as biomarkers of sepsis development and therapeutic targets.

Scientific reports Jul 8, 2025
The treatment of sepsis is challenging due to unclear mechanisms. Propionate is increasingly seen as critical to sepsis pathophysiology by bridging gut microbiota and immunity, but the mechanisms remain unclear. Our study analysed differences in prop...
Sepsis Single-Cell Analysis Gene Regulatory Networks Transcription Factors Male Leukocytes, Mononuclear Gene Expression Profiling Propionates Humans Biomarkers
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Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks.

PloS one Jul 8, 2025
PURPOSE: Machine learning is a powerful tool to develop algorithms for clinical diagnosis. However, standard machine learning algorithms are not perfectly suited for clinical data since the data are interconnected and may contain time series. As show...
Algorithms Sepsis Blood Cell Count Graph Neural Networks Humans Neural Networks, Computer Machine Learning
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Combination of machine learning and protein‑protein interaction network established one ATM‑DPP4‑TXN ferroptotic diagnostic model with experimental validation.

Molecular medicine reports Jul 4, 2025
Ferroptosis and lethal sepsis are interlinked, although this association remains largely unknown to clinical panels. Sepsis is characterized by dysfunction of the inflammatory microenvironment. Most septic biomarkers lack independent validation, and ...
Databases, Genetic Ferroptosis Dipeptidyl Peptidase 4 Ataxia Telangiectasia Mutated Proteins Thioredoxins Sepsis Protein Interaction Maps Gene Regulatory Networks Computational Biology Machine Learning Biomarkers Gene Expression Profiling Humans
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5-Hydroxymethylcytosine signatures as diagnostic biomarkers for septic cardiomyopathy.

Scientific reports Jul 4, 2025
At present, there are currently no molecular biomarkers for the early diagnosis of sepsis cardiomyopathy (SCM) in clinical practice. This study focuses on an in-depth examination of the DNA hydroxymethylation profiles within plasma extracellular vesi...
Male Middle Aged Humans DNA Methylation Sepsis Biomarkers Cardiomyopathies Extracellular Vesicles Female 5-Methylcytosine Aged
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Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling.

Scientific reports Jul 1, 2025
Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rates and complex patient responses. Accurately predicting patient outcomes is essential for optimizing...
Middle Aged Sepsis Survival Analysis Proportional Hazards Models Prognosis Female Humans Aged Machine Learning Male
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Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions.

Pediatric emergency care Jul 1, 2025
Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leading cause of childhood mortality. The broad adoption of electronic health records combined with rapid...
Humans Artificial Intelligence Machine Learning Electronic Health Records Sepsis Child
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Identification of key genes and development of an identifying machine learning model for sepsis.

Inflammation research : official journal of the European Histamine Research Society ... [et al.] Jun 30, 2025
OBJECTIVE AND DESIGN: This study aims to identify key genes of sepsis and construct a model for sepsis identification through integrated multi-organ single-cell RNA sequencing (scRNA-seq) and machine learning.
Humans Databases, Genetic Machine Learning Animals Single-Cell Analysis Lung Mice Gene Expression Profiling Sequence Analysis, RNA Sepsis Support Vector Machine
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Comprehensive analysis and experimental validation of BST1 as a novel diagnostic biomarker for pediatric sepsis using multiple machine learning algorithms.

European journal of pediatrics Jun 25, 2025
Bone marrow stromal cell antigen-1 (BST1) expression is elevated in a variety of human diseases, but its relationship with pediatric sepsis is unclear. This study aimed to investigate the expression of BST1 in pediatric sepsis patients and its value ...
ADP-ribosyl Cyclase Sepsis GPI-Linked Proteins Receptors, Cell Surface Adolescent Isoantigens Child, Preschool Child Biomarkers Humans ROC Curve Female Machine Learning Enzyme-Linked Immunosorbent Assay Male Algorithms Antigens, CD Infant
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