AIMC Topic: Delirium
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Development of a Machine Learning-Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study.
Journal of medical Internet research
Jun 19, 2025
BACKGROUND: Delirium is a prevalent phenomenon among patients admitted to the geriatric intensive care unit (ICU) and can adversely impact prognosis and augment the risk of complications.
AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients.
PloS one
Jun 5, 2025
INTRODUCTION: In older patients, postoperative delirium (POD) is a major complication that can result in greater morbidity, longer hospital stays, and higher healthcare expenses. Accurate prediction models for POD can enhance patient outcomes by guid...
A machine learning-based prediction model for sepsis-associated delirium in intensive care unit patients with sepsis-associated acute kidney injury.
Renal failure
Jun 2, 2025
Sepsis-associated acute kidney injury (SA-AKI) patients in the ICU often suffer from sepsis-associated delirium (SAD), which is linked to unfavorable outcomes. This research aimed to develop a machine learning-based model for early SAD prediction in ...
A correlational study of plasma galectin-3 as a potential predictive marker of postoperative delirium in patients with acute aortic dissection.
Scientific reports
May 8, 2025
This study aimed to demonstrate whether plasma galectin-3 could predict the development of postoperative delirium (POD) in patients with acute aortic dissection (AAD). Prospective, observational study. Cardiac surgery intensive care unit. Consecutive...
Machine Learning Multimodal Model for Delirium Risk Stratification.
JAMA network open
May 1, 2025
IMPORTANCE: Automating the identification of risk for developing hospital delirium with models that use machine learning (ML) could facilitate more rapid prevention, identification, and treatment of delirium. However, there are very few reports on th...
Evaluating the Chinese versions of delirium assessment scales: a diagnostic systematic review.
BMC psychiatry
Apr 28, 2025
BACKGROUND: The purpose of this study is to examine the validity, reliability and methodological quality of delirium scales that have been translated and adapted in China using quality assessment tools.
Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data.
BMC infectious diseases
Apr 23, 2025
OBJECTIVE: The aim of this study was to construct interpretable machine learning models to predict the risk of developing delirium in patients with sepsis and to explore the impact of delirium on the 28-day survival rate of patients.
Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation.
JMIR medical informatics
Apr 18, 2025
BACKGROUND: Delirium is common in hospitalized patients and is correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screeni...
Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated delirium.
PloS one
Apr 9, 2025
This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. On the whole, a cohort of 3,197 SAD patients were collected from the Medical Information Mart for...
Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.
Journal of medical Internet research
Apr 2, 2025
BACKGROUND: Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Developing an accurate, real-time prediction model for delirium represents an advancement in critical car...
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