AIMC Topic: Delirium

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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...
Humans Deep Learning Delirium Critical Care Neural Networks, Computer Electronic Health Records
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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...
Humans Patient Discharge Intensive Care Units Delirium Aged Deep Learning Critical Illness Electroencephalography
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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.
Artificial Intelligence Retrospective Studies Female Humans Delirium Middle Aged Male Hospitalization Body Composition Adult
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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.
Intensive Care Units Deep Learning Delirium Retrospective Studies Critical Illness Alberta Humans
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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.
Reproducibility of Results Adult Delirium ROC Curve Humans Machine Learning Prospective Studies
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Confusion regarding the use of Natural Language Processing in ICU delirium assessment. Author's reply.

Intensive care medicine May 23, 2022
Humans Natural Language Processing Intensive Care Units Delirium Reproducibility of Results
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Confusion regarding the use of Natural Language Processing in ICU delirium assessment.

Intensive care medicine May 13, 2022
Natural Language Processing Delirium Intensive Care Units Reproducibility of Results Humans
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Natural language processing and detecting delirium.

The Lancet. Respiratory medicine May 11, 2022
Natural Language Processing Delirium Humans
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Postoperative delirium prediction using machine learning models and preoperative electronic health record data.

BMC anesthesiology Jan 3, 2022
BACKGROUND: Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) d...
Cohort Studies Predictive Value of Tests Preoperative Period Delirium Male Retrospective Studies Middle Aged Electronic Health Records Postoperative Complications Female Reproducibility of Results Humans Aged Machine Learning
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Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study.

Journal of medical systems Mar 1, 2021
Early identification of patients with life-threatening risks such as delirium is crucial in order to initiate preventive actions as quickly as possible. Despite intense research on machine learning for the prediction of clinical outcomes, the accepta...
Australia Clinical Decision-Making Algorithms Diagnosis, Differential Female Humans Electronic Health Records Male Delirium Psychiatric Status Rating Scales Diagnostic Errors Middle Aged Machine Learning Pilot Projects
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