AIMC Topic: Intensive Care Units
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Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning.
Cardiovascular diabetology
Nov 26, 2024
BACKGROUND: The burden of atrial fibrillation (AF) in the intensive care unit (ICU) remains heavy. Glycaemic control is important in the AF management. Glycaemic variability (GV), an emerging marker of glycaemic control, is associated with unfavourab...
Female
Cause of Death
Reproducibility of Results
Atrial Fibrillation
Risk Factors
Glycemic Control
Aged
Blood Glucose
Humans
Prognosis
Time Factors
Aged, 80 and over
Machine Learning
Retrospective Studies
Hospital Mortality
Male
Decision Support Techniques
Critical Illness
Middle Aged
Predictive Value of Tests
Databases, Factual
Risk Assessment
Intensive Care Units
Biomarkers
Effect of a Machine Learning-Derived Early Warning Tool With Treatment Protocol on Hypotension During Cardiac Surgery and ICU Stay: The Hypotension Prediction 2 (HYPE-2) Randomized Clinical Trial.
Critical care medicine
Nov 22, 2024
OBJECTIVES: Cardiac surgery is associated with perioperative complications, some of which might be attributable to hypotension. The Hypotension Prediction Index (HPI), a machine-learning-derived early warning tool for hypotension, has only been evalu...
A novel classical machine learning framework for early sepsis prediction using electronic health record data from ICU patients.
Computers in biology and medicine
Nov 22, 2024
Sepsis, a life-threatening condition triggered by the body's response to infection, remains a significant global health challenge, annually affecting millions in the United States alone with substantial mortality and healthcare costs. Early predictio...
Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm.
Scientific reports
Nov 18, 2024
This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are min...
Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.
Scientific reports
Nov 15, 2024
OBJECTIVE: The objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS.
Assessing COVID-19 Vaccine Effectiveness and Risk Factors for Severe Outcomes through Machine Learning Techniques: A Real-World Data Study in Andalusia, Spain.
Journal of epidemiology and global health
Nov 11, 2024
BACKGROUND: COVID-19 vaccination has become a pivotal global strategy in managing the pandemic. Despite COVID-19 no longer being classified as a Public Health Emergency of International Concern, the virus continues affecting people worldwide. This st...
Explainable machine learning for early prediction of sepsis in traumatic brain injury: A discovery and validation study.
PloS one
Nov 11, 2024
BACKGROUND: People with traumatic brain injury (TBI) are at high risk for infection and sepsis. The aim of the study was to develop and validate an explainable machine learning(ML) model based on clinical features for early prediction of the risk of ...
Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.
Scientific reports
Nov 8, 2024
Given the limited capacity to accurately determine the necessity for intubation in intensive care unit settings, this study aimed to develop and externally validate an interpretable machine learning model capable of predicting the need for intubation...
Impact of nutrition-related laboratory tests on mortality of patients who are critically ill using artificial intelligence: A focus on trace elements, vitamins, and cholesterol.
Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition
Oct 25, 2024
BACKGROUND: This study aimed to understand the collective impact of trace elements, vitamins, cholesterol, and prealbumin on patient outcomes in the intensive care unit (ICU) using an advanced artificial intelligence (AI) model for mortality predicti...
Enabling machine learning models in alarm fatigue research: Creation of a large relevance-annotated oxygen saturation alarm data set.
Computers in biology and medicine
Oct 24, 2024
BACKGROUND: Too many unnecessary alarms in the intensive care unit are one of the main reasons for alarm fatigue: Medical staff is overburdened and fails to respond appropriately. This endangers both patients and staff. Currently, there are no algori...
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