AIMC Topic: Sepsis
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Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.
Nature medicine
Apr 2, 2025
The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pr...
Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.
BMC medical informatics and decision making
Mar 31, 2025
BACKGROUND: Sepsis, a severe systemic response to infection, frequently results in adverse outcomes, underscoring the urgency for prompt and accurate prognostic tools. Machine learning methods such as logistic regression, random forests, and CatBoost...
Constructing an early warning model for elderly sepsis patients based on machine learning.
Scientific reports
Mar 27, 2025
Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial intelligence shows benefits in early warning. The aim of the study was to construct an early machin...
Large Language Model-Driven Knowledge Graph Construction in Sepsis Care Using Multicenter Clinical Databases: Development and Usability Study.
Journal of medical Internet research
Mar 27, 2025
BACKGROUND: Sepsis is a complex, life-threatening condition characterized by significant heterogeneity and vast amounts of unstructured data, posing substantial challenges for traditional knowledge graph construction methods. The integration of large...
AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters.
Computers in biology and medicine
Mar 15, 2025
Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk of infections. Neutropenic sepsis is a life-threatening condition and a major cause of cancer-related mortality. Patients with neutropenic sepsis are...
Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.
Scientific reports
Mar 11, 2025
Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditional inflammatory markers facilitate the diagnosis of sepsis, the aspect of immune suppression remains ...
Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.
Frontiers in immunology
Mar 10, 2025
INTRODUCTION: Sepsis, a critical medical condition resulting from an irregular immune response to infection, leads to life-threatening organ dysfunction. Despite medical advancements, the critical need for research into dependable diagnostic markers ...
An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis.
JMIR human factors
Mar 4, 2025
BACKGROUND: Antimicrobial resistances pose significant challenges in health care systems. Clinical decision support systems (CDSSs) represent a potential strategy for promoting a more targeted and guideline-based use of antibiotics. The integration o...
Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.
Frontiers in immunology
Mar 3, 2025
BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to develop and validate a machine learning (ML) model to predict SIC risk in patients with sepsis.
Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database.
BMJ open
Feb 26, 2025
OBJECTIVE: This study developed and validated a stacked ensemble machine learning model to predict the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis.
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