AIMC Topic: Critical Illness

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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...
Middle Aged Risk Assessment Blood Glucose Time Factors Hospital Mortality Risk Factors Aged Aged, 80 and over Male Predictive Value of Tests Cause of Death Atrial Fibrillation Glycemic Control Decision Support Techniques Databases, Factual Biomarkers Prognosis Retrospective Studies Critical Illness Intensive Care Units Female Reproducibility of Results Humans Machine Learning
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Factors and machine learning models for predicting successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury: a retrospective cohort study based on MIMIC-IV database.

BMC nephrology Nov 12, 2024
BACKGROUND: For critically ill patients with acute kidney injury (AKI), there remains controversy regarding the predictive factors affecting the discontinuation of continuous renal replacement therapy (CRRT). This study aims to explore factors associ...
Humans Machine Learning Databases, Factual Retrospective Studies Continuous Renal Replacement Therapy Acute Kidney Injury Critical Illness Female Aged Male Middle Aged Cohort Studies Withholding Treatment
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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...
Retrospective Studies Critical Illness Intensive Care Units Female Aged ROC Curve Humans Vitamins Artificial Intelligence Cholesterol Male Nutritional Status Middle Aged Prealbumin Trace Elements Hospital Mortality
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Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study.

Journal of critical care Oct 2, 2024
BACKGROUND: Upper gastrointestinal bleeding (UGIB) is a significant cause of morbidity and mortality worldwide. This study investigates the use of residual variables and machine learning (ML) models for predicting major bleeding in patients with seve...
Humans Critical Illness Middle Aged Machine Learning Intensive Care Units Gastrointestinal Hemorrhage Algorithms Female Aged Prognosis Male
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From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit.

Intensive care medicine Sep 12, 2024
PURPOSE: Despite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benef...
Intensive Care Units, Pediatric Intensive Care Units, Neonatal Artificial Intelligence Critical Illness Infant, Newborn Child Humans
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A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients.

PloS one Sep 4, 2024
BACKGROUND: Mechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a predictive model to estimate hospital mortality among MV patients, utilizing comprehensive health d...
Middle Aged Intensive Care Units Respiration, Artificial Humans Female Hospital Mortality Machine Learning Aged Support Vector Machine Databases, Factual Male Critical Illness
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Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms.

Annals of medicine Aug 19, 2024
BACKGROUND: To construct and evaluate a predictive model for in-hospital mortality among critically ill patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT), based on nine machine learning (ML) algorithm.
Continuous Renal Replacement Therapy Acute Kidney Injury Critical Illness Intensive Care Units Female Aged Male Middle Aged Risk Assessment Humans Machine Learning Algorithms Retrospective Studies Hospital Mortality ROC Curve Nomograms United States
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Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis.

Cell reports. Medicine Aug 9, 2024
Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingl...
Female Aged Male Middle Aged Sepsis Humans Drug Monitoring Machine Learning Organ Dysfunction Scores Algorithms beta-Lactams Anti-Bacterial Agents Critical Illness
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Spectrochemical and explainable artificial intelligence approaches for molecular level identification of the status of critically ill patients with COVID-19.

Talanta Jul 31, 2024
This study explores the molecular alterations and disease progression in COVID-19 patients using ATR-FTIR spectroscopy combined with spectrochemical and explainable artificial intelligence (XAI) approaches. Blood serum samples from intubated patients...
Adult Male Humans Middle Aged Critical Illness Spectroscopy, Fourier Transform Infrared Artificial Intelligence Female Severity of Illness Index COVID-19 SARS-CoV-2 Glycosylation Biomarkers Aged Immunoglobulin G
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A machine learning-based predictive model for the in-hospital mortality of critically ill patients with atrial fibrillation.

International journal of medical informatics Jul 31, 2024
BACKGROUND: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and significantly raises the in-hospital mortality rate. Existing scoring systems or models have limited predictive capabilities for AF patients in ICU. Our study...
Male Critical Illness Risk Assessment Female Hospital Mortality Humans Reproducibility of Results Atrial Fibrillation Machine Learning Aged Computer Simulation Aged, 80 and over
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