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Intensive Care Units

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Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.

Neurocritical care
BACKGROUND: The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to demonstrate that intensive care unit (ICU) variables are not efficient to predict 6-month functional ou...

Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction.

Critical care medicine
Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction are increasingly used. Therefore, understanding risks and benefits of such algorithms is relevant at the bedside. ARDS is a complex and severe lung co...

Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Cardiac arrest (CA) is a global public health challenge. This study explored the predictors of mortality and their interactions utilizing machine learning algorithms and their related mortality odds among patients following CA. MATERIAL AN...

Prediction of 30-day mortality for ICU patients with Sepsis-3.

BMC medical informatics and decision making
BACKGROUND: There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mortality prediction for Sepsis...

Predicting Tracheostomy Need on Admission to the Intensive Care Unit-A Multicenter Machine Learning Analysis.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: It is difficult to predict which mechanically ventilated patients will ultimately require a tracheostomy which further predisposes them to unnecessary spontaneous breathing trials, additional time on the ventilator, increased costs, and fu...

Role of artificial intelligence in critical care nutrition support and research.

Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition
Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medic...

Evaluating the effectiveness of a sliding window technique in machine learning models for mortality prediction in ICU cardiac arrest patients.

International journal of medical informatics
Extensive research has been devoted to predicting ICU mortality, to assist clinical teams managing critical patients. Electronic health records (EHR) contain both static and dynamic medical data, with the latter accumulating during ICU stays. Existin...

Adopting machine learning to predict ICU delirium.

Neurosurgical review
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...

Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia.

Scientific reports
Severe pneumonia results in high morbidity and mortality despite advanced treatments. This study investigates thoracic muscle mass from chest CT scans as a biomarker for predicting clinical outcomes in ICU patients with severe pneumonia. Analyzing el...