AIMC Topic: Critical Illness
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[Artificial intelligence and acute kidney injury].
Medizinische Klinik, Intensivmedizin und Notfallmedizin
Feb 23, 2024
Digitalization is increasingly finding its way into intensive care units and with it artificial intelligence (AI) for critically ill patients. One promising area for the use of AI is in the field of acute kidney injury (AKI). The use of AI is primari...
INTERPRETABLE MACHINE LEARNING FOR PREDICTING RISK OF INVASIVE FUNGAL INFECTION IN CRITICALLY ILL PATIENTS IN THE INTENSIVE CARE UNIT: A RETROSPECTIVE COHORT STUDY BASED ON MIMIC-IV DATABASE.
Shock (Augusta, Ga.)
Feb 20, 2024
The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for...
Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study.
European journal of clinical investigation
Feb 20, 2024
BACKGROUND: Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer th...
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...
Measuring Implicit Bias in ICU Notes Using Word-Embedding Neural Network Models.
Chest
Jan 8, 2024
BACKGROUND: Language in nonmedical data sets is known to transmit human-like biases when used in natural language processing (NLP) algorithms that can reinforce disparities. It is unclear if NLP algorithms of medical notes could lead to similar trans...
Applications of natural language processing at emergency department triage: A narrative review.
PloS one
Dec 14, 2023
INTRODUCTION: Natural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this ...
Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults.
Chest
Nov 1, 2023
BACKGROUND: Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur.
Continuous visualization and validation of pain in critically ill patients using artificial intelligence: a retrospective observational study.
Scientific reports
Oct 14, 2023
Machine learning tools have demonstrated viability in visualizing pain accurately using vital sign data; however, it remains uncertain whether incorporating individual patient baselines could enhance accuracy. This study aimed to investigate improvin...
Unmasking Critical Illness: Using Machine Learning and Biomarkers to See What Lies Beneath.
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
Oct 3, 2023
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...
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