AIMC Topic: Delirium
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Enhanced machine learning predictive modeling for delirium in elderly ICU patients with COPD and respiratory failure: A retrospective study based on MIMIC-IV.
PloS one
Mar 20, 2025
BACKGROUND AND OBJECTIVE: Elderly patients with Chronic obstructive pulmonary disease (COPD) and respiratory failure admitted to the intensive care unit (ICU) have a poor prognosis, and the occurrence of delirium further worsens outcomes and increase...
Development of a Disease Model for Predicting Postoperative Delirium Using Combined Blood Biomarkers.
Annals of clinical and translational neurology
Mar 17, 2025
OBJECTIVE: Postoperative delirium, a common neurocognitive complication after surgery and anesthesia, requires early detection for potential intervention. Herein, we constructed a multidimensional postoperative delirium risk-prediction model incorpor...
Machine Learning-Based Prediction of Delirium and Risk Factor Identification in Intensive Care Unit Patients With Burns: Retrospective Observational Study.
JMIR formative research
Mar 5, 2025
BACKGROUND: The incidence of delirium in patients with burns receiving treatment in the intensive care unit (ICU) is high, reaching up to 77%, and has been associated with increased mortality rates. Therefore, early identification of patients at high...
Functional MRI-based machine learning strategy for prediction of postoperative delirium in cardiac surgery patients: A secondary analysis of a prospective observational study.
Journal of clinical anesthesia
Feb 13, 2025
STUDY OBJECTIVE: Delirium is a common complication after cardiac surgery and is associated with poor prognosis. An effective delirium prediction model could identify high-risk patients who might benefit from targeted prevention strategies. We introdu...
Development and usability evaluation of a nurse-led clinical decision support system (AI-AntiDelirium) for management of intensive care unit delirium: A mixed methods study.
Applied nursing research : ANR
Feb 4, 2025
BACKGROUND: Clinical decision support systems (CDSS) have been identified to aid clinical decision-making, but few studies focus on the application of CDSS in intensive care unit (ICU) delirium, and particularly usability testing is not employed. We ...
Development of a LASSO machine learning algorithm-based model for postoperative delirium prediction in hepatectomy patients.
BMC surgery
Jan 15, 2025
OBJECTIVE: The objective of this study was to develop and validate a clinically applicable nomogram for predicting the risk of delirium following hepatectomy.
Application of machine learning for delirium prediction and analysis of associated factors in hospitalized COVID-19 patients: A comparative study using the Korean Multidisciplinary cohort for delirium prevention (KoMCoDe).
International journal of medical informatics
Dec 1, 2024
BACKGROUND: The incidence of delirium in hospitalized coronavirus disease 2019 (COVID-19) patients is linked to adverse health outcomes. Predicting the occurrence and risk factors of delirium is key to preventing its sudden onset.
Machine Learning for Prediction of Postoperative Delirium in Adult Patients: A Systematic Review and Meta-analysis.
Clinical therapeutics
Oct 11, 2024
PURPOSE: This meta-analysis aimed to evaluate the performance of machine learning (ML) models in predicting postoperative delirium (POD) and to provide guidance for clinical application.
Adopting machine learning to predict ICU delirium.
Neurosurgical review
Jul 26, 2024
With neuropsychiatric complications recognized among COVID-19 patients translating into significant morbidity, we explore the current state-of-the-art for auto Machine Learning (ML) to predict ICU delirium among severe COVID-19 patients which has bee...
Machine learning for the prediction of delirium in elderly intensive care unit patients.
European geriatric medicine
Jun 28, 2024
PURPOSE: This study aims to develop and validate a prediction model for delirium in elderly ICU patients and help clinicians identify high-risk patients at the early stage.
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