AIMC Topic: Delirium
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Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort.
Scientific reports
May 20, 2024
This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significan...
Development and validation of a machine learning model to predict postoperative delirium using a nationwide database: A retrospective, observational study.
Journal of clinical anesthesia
Apr 27, 2024
STUDY OBJECTIVE: Postoperative delirium is a neuropsychological syndrome that typically occurs in surgical patients. Its onset can lead to prolonged hospitalization as well as increased morbidity and mortality. Therefore, it is important to promptly ...
Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups.
International journal of clinical pharmacy
Apr 9, 2024
The advent of artificial intelligence (AI) technologies has taken the world of science by storm in 2023. The opportunities of this easy to access technology for clinical pharmacy research are yet to be fully understood. The development of a custom-ma...
Machine learning methods for developing a predictive model of the incidence of delirium in cardiac intensive care units.
Revista espanola de cardiologia (English ed.)
Jan 16, 2024
INTRODUCTION AND OBJECTIVES: Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predi...
Preoperative Delirium Risk Screening in Patients Undergoing a Cardiac Surgery: Results from the Prospective Observational FINDERI Study.
The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
Dec 29, 2023
OBJECTIVE: Postoperative delirium (POD) is a common complication of cardiac surgery that is associated with higher morbidity, longer hospital stay, cognitive decline, and mortality. Preoperative assessments may help to identify patients´ POD risk. Ho...
An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care.
Artificial intelligence in medicine
Sep 14, 2023
Deep Learning (DL) models have received increasing attention in the clinical setting, particularly in intensive care units (ICU). In this context, the interpretability of the outcomes estimated by the DL models is an essential step towards increasing...
Supervised deep learning with vision transformer predicts delirium using limited lead EEG.
Scientific reports
May 16, 2023
As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool. EEG is the criterion standa...
The association of radiologic body composition parameters with clinical outcomes in level-1 trauma patients.
European journal of trauma and emergency surgery : official publication of the European Trauma Society
Mar 2, 2023
PURPOSE: The present study aims to assess whether CT-derived muscle mass, muscle density, and visceral fat mass are associated with in-hospital complications and clinical outcome in level-1 trauma patients.
Deep Learning-Based Recurrent Delirium Prediction in Critically Ill Patients.
Critical care medicine
Feb 15, 2023
OBJECTIVES: To predict impending delirium in ICU patients using recurrent deep learning.
Machine Learning-Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis.
Journal of the American Medical Directors Association
Jul 31, 2022
OBJECTIVE: To critically appraise and quantify the performance studies by employing machine learning (ML) to predict delirium.
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