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Intensive Care Units

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Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.

International wound journal
Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life...

The Evolution and Future of Intensive Care Management in the Era of Telecritical Care and Artificial Intelligence.

Current problems in cardiology
Critical care practice has been embodied in the healthcare system since the institutionalization of intensive care units (ICUs) in the late '50s. Over time, this sector has experienced many changes and improvements in providing immediate and dedicate...

Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach.

Journal of biomedical informatics
OBJECTIVE: Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor...

Supervised deep learning with vision transformer predicts delirium using limited lead EEG.

Scientific reports
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...

Artificial Intelligence in Intensive Care Medicine: Toward a ChatGPT/GPT-4 Way?

Annals of biomedical engineering
Although intensive care medicine (ICM) is a relatively young discipline, it has rapidly developed into a full-fledged and highly specialized specialty covering several fields of medicine. The COVID-19 pandemic led to a surge in intensive care unit de...

A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients.

BMC medical informatics and decision making
BACKGROUND: A growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce the use of social and medical resources. Artificial intelligence (AI) technology is increasingly being...

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.

Critical care (London, England)
BACKGROUND: Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standar...

Comparison of correctly and incorrectly classified patients for in-hospital mortality prediction in the intensive care unit.

BMC medical research methodology
BACKGROUND: The use of machine learning is becoming increasingly popular in many disciplines, but there is still an implementation gap of machine learning models in clinical settings. Lack of trust in models is one of the issues that need to be addre...

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit.

BMC neurology
INTRODUCTION: Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins have been utilized to track wind turbines' operations, monitor spacecraft's status, and even create...

Multi-event survival analysis through dynamic multi-modal learning for ICU mortality prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Survival analysis is widely applied for assessing the expected duration of patient status towards event occurrences such as mortality in healthcare domain, which is generally considered as a time-to-event problem. Patients w...