AIMC Topic: Sepsis
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Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.
Computer methods and programs in biomedicine
Jun 1, 2025
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
May 30, 2025
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...
A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.
Nature communications
May 27, 2025
Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays in diagnosis have substantial impact on survival. Herein, blood samples from 586 in-house patients with suspected sepsis are used in conjunction with ...
ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.
Critical care (London, England)
May 26, 2025
BACKGROUND: Major Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered outcome for assessing the impact of acute kidney injury (AKI). Existing prediction models for MAKE30 are static and overlook dynamic changes in clinical ...
Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study.
Journal of medical Internet research
May 26, 2025
BACKGROUND: Sepsis-associated liver injury (SALI) is a severe complication of sepsis that contributes to increased mortality and morbidity. Early identification of SALI can improve patient outcomes; however, sepsis heterogeneity makes timely diagnosi...
Bio inspired feature selection and graph learning for sepsis risk stratification.
Scientific reports
May 22, 2025
Sepsis remains a leading cause of mortality in critical care settings, necessitating timely and accurate risk stratification. However, existing machine learning models for sepsis prediction often suffer from poor interpretability, limited generalizab...
Prediction of bloodstream infection using machine learning based primarily on biochemical data.
Scientific reports
May 20, 2025
Early diagnosis of bloodstream infection (BSI) is crucial for informed antibiotic use. This study developed a machine learning approach for early BSI detection using a comprehensive dataset from Rigshospitalet, Denmark (2010-2020). The dataset includ...
Enhancing Clinical Decision Support: A Heuristic Evaluation of Explainable AI in Healthcare Dashboards.
Studies in health technology and informatics
May 15, 2025
Explainable Artificial Intelligence (XAI) is crucial for enhancing transparency, interpretability and actionability of AI systems, particularly in healthcare. The SAD XAI Dashboard, a clinical decision support (CDS) tool for sepsis-associated deliriu...
Assessing Healthcare Stakeholder Understanding of Machine Learning Documentation.
Studies in health technology and informatics
May 15, 2025
Artificial Intelligence (AI) has significantly advanced clinical decision support systems in healthcare, particularly using Machine Learning (ML) models. However, the technical nature of current ML model documentation often leads to lack of comprehen...
Predicting sepsis treatment decisions in the paediatric emergency department using machine learning: the AiSEPTRON study.
BMJ paediatrics open
May 14, 2025
BACKGROUND: Early identification of children at risk of sepsis in emergency departments (EDs) is crucial for timely treatment and improved outcomes. Existing risk scores and criteria for paediatric sepsis are not well-suited for early diagnosis in ED...
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