AIMC Topic: Intensive Care Units

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Development and external validation of a prediction model for prolonged intensive care unit stay in heart failure patients.

European journal of cardiovascular nursing
AIMS: Prolonged intensive care unit (ICU) stays in heart failure patients are associated with poor prognosis and result in high medical expenses. To develop and validate a predictive model for prolonged ICU stays in heart failure patients.

When Machines Decide: Exploring How Trust in AI Shapes the Relationship Between Clinical Decision Support Systems and Nurses' Decision Regret: A Cross-Sectional Study.

Nursing in critical care
BACKGROUND: Artificial intelligence (AI)-based Clinical Decision Support Systems (AI-CDSS) are increasingly implemented in intensive care settings to support nurses in complex, time-sensitive decisions, aiming to improve accuracy, efficiency and pati...

Patient radiation safety in the intensive care unit.

The British journal of radiology
The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiological practices to limit radiation exposure in intensive care unit (ICU) patients. Radiological imaging...

Predicting Length of Stay in Acute Care Using Day-to-Day Patient Information.

Studies in health technology and informatics
Predicting the Length of Stay (LoS) in healthcare settings is a critical task that supports optimized resource allocation and tailored clinical decision-making. Unlike most studies focused on ICU patients, this work targets acute care settings, addre...

ICU Length of Stay Prediction for Patients with Diabetes Using Machine Learning and Clinical Notes.

Studies in health technology and informatics
Diabetes, a chronic disease, often leads to poor health outcomes and increased healthcare costs, particularly for patients admitted to ICU. Accurate early prediction of ICU length of stay (LOS) is vital for hospital resource management and patient ou...

From Internal Validation to External Validation: An Artificial Intelligence-Based Study on Predicting Optimal Timing for Mechanical Ventilation Weaning in ICU Patients.

Studies in health technology and informatics
Mechanical ventilation weaning is critical for ICU patients, as prolonged or premature use can cause adverse outcomes and resource waste. Using six years of ICU data, Chi Mei Medical Center developed two-stage AI predictive models to optimize the tim...

Interpretable Machine Learning Prediction Model for Predicting Mortality Risk of ICU Patients With Pressure Ulcers Based on the Braden Scale: A Clinical Study Based on MIMIC-IV.

Journal of clinical nursing
AIMS: This study was to create an interpretable machine learning model to predict the risk of mortality within 90 days for ICU patients suffering from pressure ulcers.

Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models.

Computers in biology and medicine
The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decision support. This study introduces a novel framework, StreamHealth Multi-Horizon AI, which has been de...

CLABpredICU---AI-driven risk prediction for CLABSI in intensive care units based on clinical and biochemical parameters.

American journal of infection control
BACKGROUND: Central line--associated bloodstream infections (CLABSI) are major causes of morbidity and mortality in intensive care units. This study aimed to develop an artificial intelligence-driven predictive model for CLABSI within 2 calendar days...

Understanding deep learning models for Length of Stay prediction on critically ill patients through latent space visualization.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Continuous, real-time monitoring of Length of Stay (LoS) for critically ill patients in Intensive Care Units (ICUs) is essential for anticipating patient needs, reduce the risk of adverse events, optimize resource allocation...