Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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Subcategories: Sepsis
Showing 4138-4158 of 7,492 articles
Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry.

BACKGROUND: Shape is a critical element of the visual appeal of strawberry fruit and is influenced b...

Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm.

BACKGROUND: Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) ...

An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing.

BACKGROUND: Opioid-induced respiratory depression (OIRD) is traditionally recognized by assessment o...

Machine learning in nephrology: scratching the surface.

Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases....

Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Digitization of healthcare will be a major innovation driver in the coming decade. Also, enabled by ...

Temporal convolutional networks allow early prediction of events in critical care.

OBJECTIVE: Clinical interventions and death in the intensive care unit (ICU) depend on complex patte...

Acceptability and Perceived Utility of Telemedical Consultation during Cardiac Arrest Resuscitation. A Multicenter Survey.

Many clinicians who participate in or lead in-hospital cardiac arrest (IHCA) resuscitations lack co...

[A review on multi-modal human motion representation recognition and its application in orthopedic rehabilitation training].

Human motion recognition (HAR) is the technological base of intelligent medical treatment, sports tr...

[Prognostic model of small sample critical diseases based on transfer learning].

Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic mo...

A deep neural network based hierarchical multi-label classification method.

With the accumulation of data generated by biological experimental instruments, using hierarchical m...

Postimplementation Evaluation of a Machine Learning-Based Deterioration Risk Alert to Enhance Sepsis Outcome Improvements.

Machine learning-based early warning systems (EWSs) can detect clinical deterioration more accuratel...

Successful Resuscitation of a Young Girl Who Drank Rivastigmine With Respiratory Failure.

Rivastigmine is a non-competitive reversible inhibitor of acetylcholinesterase which is approved as ...

PEEP guided by electrical impedance tomography during one-lung ventilation in elderly patients undergoing thoracoscopic surgery.

BACKGROUND: To examine the influence of positive end-expiratory pressure (PEEP) settings on lung mec...

Respiratory parameters and acute kidney injury in acute respiratory distress syndrome: a causal inference study.

BACKGROUND: Assess the respiratory-related parameters associated with subsequent severe acute kidney...

Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

OBJECTIVE: To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns...

ML-Net: multi-label classification of biomedical texts with deep neural networks.

OBJECTIVE: In multi-label text classification, each textual document is assigned 1 or more labels. A...

Predicting weaning difficulty for planned extubation patients with an artificial neural network.

This study aims to construct a neural network to predict weaning difficulty among planned extubation...

[Artificial intelligence and nursing care: reflections in psychiatry].

A key government priority, artificial intelligence (IA) in healthcare is a real opportunity for nurs...

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