AIMC Topic: Intensive Care Units
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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP.
BMC medical informatics and decision making
Jul 15, 2025
BACKGROUND: Hemorrhage is a prevalent and critical condition in the intensive care unit (ICU), characterized by high incidence, elevated mortality rates, and substantial therapeutic challenges. Accurate prediction of mortality in patients with hemorr...
Advancing shock prediction: leveraging prior knowledge and self-controlled data for enhanced model accuracy and generalizability.
BMC medical informatics and decision making
Jul 14, 2025
OBJECTIVES: Timely intervention in shock is vital, as delays over one hour greatly increase mortality. This study aims to develop an enhanced machine learning model that improves predictive performance by utilizing self-controlled data and applying f...
Bias in vital signs? Machine learning models can learn patients' race or ethnicity from the values of vital signs alone.
BMJ health & care informatics
Jul 10, 2025
OBJECTIVES: To investigate whether machine learning (ML) algorithms can learn racial or ethnic information from the vital signs alone.
AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients.
Journal of medical Internet research
Jul 10, 2025
BACKGROUND: One of the main challenges with COVID-19 has been that although there are known factors associated with a worse prognosis, clinicians have been unable to predict which patients, with similar risk factors, will die or require intensive car...
Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.
Critical care (London, England)
Jul 8, 2025
Artificial Intelligence (AI) is rapidly transforming the landscape of critical care, offering opportunities for enhanced diagnostic precision and personalized patient management. However, its integration into ICU clinical practice presents significan...
Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts.
BMC medical informatics and decision making
Jul 4, 2025
BACKGROUND: The incidence of intensive care unit (ICU) admissions and the corresponding mortality rates among cancer patients are both high. However, the existing scoring systems all lack specificity. This research seeks to establish and validate a p...
Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study.
BMC medical informatics and decision making
Jul 3, 2025
BACKGROUND: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient co...
Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.
Journal of medical Internet research
Jul 3, 2025
BACKGROUND: Traumatic brain injury (TBI) is a critically ill disease with a high mortality rate, and clinical treatment is committed to continuously optimizing treatment strategies to improve survival rates.
Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.
Scientific reports
Jul 2, 2025
This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency department (ED). We conducted a retrospective study analyzing adult ED visits at a tertiary hospital. ...
Development and validation of a modified SOFA score for mortality prediction in candidemia patients.
Scientific reports
Jul 1, 2025
Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk stratification is crucial for timely intervention and could improve patient outcomes. This study aimed...
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