AIMC Topic: Intensive Care Units

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Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence.

Computers in biology and medicine
BACKGROUND: In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive cl...

Deep learning-based prediction of in-hospital mortality for sepsis.

Scientific reports
As a serious blood infection disease, sepsis is characterized by a high mortality risk and many complications. Accurate assessment of mortality risk of patients with sepsis can help physicians in Intensive Care Unit make optimal clinical decisions, w...

The Effects of Daytime Variation on Short-term Outcomes of Patients Undergoing Off-Pump Coronary Artery Bypass Grafting.

Journal of cardiothoracic and vascular anesthesia
OBJECTIVE: To evaluate the effects of time of surgery on the short-term outcomes of patients undergoing off-pump coronary artery bypass grafting (OPCABG).

Enhancing Pressure Injury Surveillance Using Natural Language Processing.

Journal of patient safety
OBJECTIVE: This study assessed the feasibility of nursing handoff notes to identify underreported hospital-acquired pressure injury (HAPI) events.

Attitudes on Artificial Intelligence use in Pediatric Care From Parents of Hospitalized Children.

The Journal of surgical research
INTRODUCTION: Artificial intelligence (AI) may benefit pediatric healthcare, but it also raises ethical and pragmatic questions. Parental support is important for the advancement of AI in pediatric medicine. However, there is little literature descri...

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.

Scientific reports
Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. ...

Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes.

Critical care (London, England)
BACKGROUND: Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients.