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Critical Care

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Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data.

PloS one
OBJECTIVE: This study aims to develop high-performing Machine Learning and Deep Learning models in predicting hospital length of stay (LOS) while enhancing interpretability. We compare performance and interpretability of models trained only on struct...

Mortality prediction using medical time series on TBI patients.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Traumatic Brain Injury (TBI) is one of the leading causes of injury-related mortality in the world, with severe cases reaching mortality rates of 30-40%. It is highly heterogeneous both in causes and consequences making more...

An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care.

Artificial intelligence in medicine
Deep Learning (DL) models have received increasing attention in the clinical setting, particularly in intensive care units (ICU). In this context, the interpretability of the outcomes estimated by the DL models is an essential step towards increasing...

Global research trends in artificial intelligence for critical care with a focus on chord network charts: Bibliometric analysis.

Medicine
BACKGROUND: The field of critical care-related artificial intelligence (AI) research is rapidly gaining interest. However, there is still a lack of comprehensive bibliometric studies that measure and analyze scientific publications on a global scale....

[The Swecrit Biobank, associated clinical registries, and machine learning (artificial intelligence) improve critical care knowledge].

Lakartidningen
The unique Swecrit Biobank and its associated clinical registries for sepsis, ARDS, cardiac arrest, trauma, and COVID-19 include more than 150,000 blood samples and descriptions of critically ill patients. These assets provide a unique opportunity to...

Continuous visualization and validation of pain in critically ill patients using artificial intelligence: a retrospective observational study.

Scientific reports
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...

Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study.

Annals of internal medicine
BACKGROUND: Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic he...

[Intelligent rehabilitation platform in intensive care unit].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases
As the development of rehabilitation medicine and critical care medicine, intensive care rehabilitation has become the focus of attention. With the development of artificial intelligence, wearable devices and non-contact multimodal behavior perceptio...

The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVE: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of p...