AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Respiration, Artificial

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Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

PloS one
BACKGROUND: Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostom...

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.

PloS one
BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide.

Prediction of weaning from mechanical ventilation using Convolutional Neural Networks.

Artificial intelligence in medicine
Weaning from mechanical ventilation covers the process of liberating the patient from mechanical support and removing the associated endotracheal tube. The management of weaning from mechanical ventilation comprises a significant proportion of the ca...

Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

BMC infectious diseases
BACKGROUND: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how...

Identifying Patient-Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning.

Sensors (Basel, Switzerland)
Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient-ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series o...

Predicting ventilator-associated pneumonia with machine learning.

Medicine
Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived ...

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

The British journal of radiology
OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the fea...

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying.

Critical care (London, England)
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate...

Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias.

British journal of anaesthesia
BACKGROUND: Artificial intelligence (AI) has the potential to personalise mechanical ventilation strategies for patients with respiratory failure. However, current methodological deficiencies could limit clinical impact. We identified common limitati...