AIMC Topic: Intensive Care Units

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A Federated Learning Model for the Prediction of Blood Transfusion in Intensive Care Units.

Studies in health technology and informatics May 15, 2025
Accurate prediction of blood transfusion requirements is crucial for patient outcomes and resource management in clinical settings. We developed a machine learning model using XGBoost to predict the need for a blood transfusion 2 hours in advance bas...
Blood Transfusion Federated Learning Humans Machine Learning Intensive Care Units
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Predicting 1-Year Survival Using Machine Learning in Very Old Patients Before ICU Admission.

Studies in health technology and informatics May 15, 2025
Discussions about the benefits of admitting very old individuals to intensive care unit (ICU) remain challenging. We hypothesized that data-driven algorithms could leverage extensive real-life data to provide more accurate long-term predictions. Our ...
Humans Machine Learning Aged, 80 and over Algorithms Prognosis Survival Rate Intensive Care Units Male Patient Admission Female
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Personalized Nutrition Strategies for Patients in the Intensive Care Unit: A Narrative Review on the Future of Critical Care Nutrition.

Nutrients May 13, 2025
Critically ill patients in intensive care units (ICUs) are at high risk of malnutrition, which can result in muscle atrophy, polyneuropathy, increased mortality, or prolonged hospitalizations with complications and higher costs during the recovery p...
Energy Intake Humans Malnutrition Precision Medicine Critical Care Nutritional Status Critical Illness Enteral Nutrition Intensive Care Units Nutritional Support Parenteral Nutrition
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Rapid Response System Restructure: Focus on Prevention and Early Intervention.

Critical care nursing quarterly May 13, 2025
This article describes the staged restructure of the rapid response program into a dedicated 24/7 proactive rapid response system in a quaternary academic medical center in the southern United States. Rapid response nurses (RRNs) completed clinical l...
Inpatients Heart Arrest Artificial Intelligence Humans Intensive Care Units Hospital Mortality United States
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Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage.

Scientific reports May 10, 2025
Patients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in predicting sepsis risk in intensive care units (ICUs) patients with ICH. We conducted a retrospective ...
Retrospective Studies Humans Sepsis Intensive Care Units Machine Learning Logistic Models Female Prognosis Cerebral Hemorrhage Risk Factors Aged Male Middle Aged
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Thirty-day mortality risk prediction for geriatric patients undergoing non-cardiac surgery in the surgical intensive care unit.

European journal of medical research May 9, 2025
BACKGROUND: The prediction of mortality for elderly patients undergoing non-cardiac surgeries is a vital research area, as accurate risk assessment can help surgeons make better clinical decisions during the perioperative period. This study aims to b...
Risk Factors Aged Aged, 80 and over Male Algorithms Humans Risk Assessment Intensive Care Units Female Surgical Procedures, Operative
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Predictive analytics for early detection of hospital-acquired complications: An artificial intelligence approach.

Health information management : journal of the Health Information Management Association of Australia May 1, 2025
BACKGROUND: Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs.
Female Aged Aged, 80 and over Adult Male Middle Aged Risk Assessment Artificial Intelligence Western Australia Operative Time Machine Learning Humans Iatrogenic Disease Decision Trees Retrospective Studies Severity of Illness Index Predictive Learning Models Early Diagnosis Intensive Care Units Length of Stay Cross Infection Logistic Models
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Fast and interpretable mortality risk scores for critical care patients.

Journal of the American Medical Informatics Association : JAMIA Apr 1, 2025
OBJECTIVE: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to brid...
Machine Learning Algorithms Critical Illness Intensive Care Units Humans Risk Assessment Hospital Mortality Critical Care
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[Establishing of mortality predictive model for elderly critically ill patients using simple bedside indicators and interpretable machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue Feb 1, 2025
OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assess...
Organ Dysfunction Scores Aged Male Hospital Mortality APACHE Critical Illness Intensive Care Units Female Humans Machine Learning Algorithms Prognosis Retrospective Studies
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Scale to predict risk for refractory septic shock based on a hybrid approach using machine learning and regression modeling.

Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias Feb 1, 2025
OBJECTIVE: To develop a scale to predict refractory septic shock (SS) based on clinical variables recorded during initial evaluations of patients.
ROC Curve Shock, Septic Regression Analysis Emergency Service, Hospital Male Middle Aged Risk Assessment Registries Norepinephrine Humans Machine Learning Retrospective Studies Intensive Care Units Female Aged
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