AIMC Topic: Critical Illness

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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...
Critical Illness Intensive Care Units Electroencephalography Patient Discharge Humans Delirium Aged Deep Learning
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Equity in Using Artificial Intelligence to Target Serious Illness Conversations for Patients With Life-Limiting Illness.

Journal of pain and symptom management Apr 11, 2023
Advance Care Planning Humans Artificial Intelligence Critical Illness Communication
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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.
Retrospective Studies Critical Illness Humans Delirium Alberta Intensive Care Units Deep Learning
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Early recognition of risk of critical adverse events based on deep neural decision gradient boosting.

Frontiers in public health Jan 26, 2023
INTRODUCTION: Perioperative critical events will affect the quality of medical services and threaten the safety of patients. Using scientific methods to evaluate the perioperative risk of critical illness is of great significance for improving the qu...
Hospitals Humans Neural Networks, Computer Machine Learning Critical Illness
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Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.

Critical care medicine Jan 20, 2023
OBJECTIVES: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill p...
Lung Deep Learning Ultrasonography Humans Critical Illness Prospective Studies Intensive Care Units
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A deep learning model to identify the fluid overload status in critically ill patients based on chest X-ray images.

Polish archives of internal medicine Jan 4, 2023
INTRODUCTION: Recent studies have highlighted adverse outcomes of fluid overload in critically ill patients. Therefore, its early recognition is essential for the management of these patients.
X-Rays Heart Failure Extravascular Lung Water Intensive Care Units Deep Learning Humans Critical Illness
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Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU.

Critical care (London, England) Jan 3, 2023
BACKGROUND: While early mobilization is commonly implemented in intensive care unit treatment guidelines to improve functional outcome, the characterization of the optimal individual dosage (frequency, level or duration) remains unclear. The aim of t...
Middle Aged Critical Care Intensive Care Units Hospitalization Early Ambulation Humans Artificial Intelligence Critical Illness
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ognitive utcomes in the ragmatic nvestigation of optimaxygen argets (CO-PILOT) trial: protocol and statistical analysis plan.

BMJ open Nov 1, 2022
INTRODUCTION: Long-term cognitive impairment is one of the most common complications of critical illness among survivors who receive mechanical ventilation. Recommended oxygen targets during mechanical ventilation vary among international guidelines....
Randomized Controlled Trials as Topic Oxygen Quality of Life Humans Cognition Critical Illness
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Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques.

Frontiers in immunology Oct 17, 2022
BACKGROUND: Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely understood. The objectives of the study were to identify the biological signature of PerCI development, ...
Organ Dysfunction Scores Humans Machine Learning Risk Factors Critical Illness ROC Curve
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Automation of sub-aortic velocity time integral measurements by transthoracic echocardiography: clinical evaluation of an artificial intelligence-enabled tool in critically ill patients.

British journal of anaesthesia Aug 26, 2022
Echocardiography Automation Cardiac Output Blood Flow Velocity Artificial Intelligence Critical Illness Humans
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