AIMC Topic: Critical Care
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Methods for estimating resting energy expenditure in intensive care patients: A comparative study of predictive equations with machine learning and deep learning approaches.
Computer methods and programs in biomedicine
Feb 9, 2025
BACKGROUND: Accurate estimation of resting energy expenditure (REE) is critical for guiding nutritional therapy in critically ill patients. While indirect calorimetry (IC) is the gold standard for REE measurement, it is not routinely feasible in clin...
Capturing Requirements for a Data Annotation Tool for Intensive Care: Experimental User-Centered Design Study.
JMIR human factors
Feb 5, 2025
BACKGROUND: Increasing use of computational methods in health care provides opportunities to address previously unsolvable problems. Machine learning techniques applied to routinely collected data can enhance clinical tools and improve patient outcom...
Multimodal convolutional neural networks for the prediction of acute kidney injury in the intensive care.
International journal of medical informatics
Feb 4, 2025
Increased monitoring of health-related data for ICU patients holds great potential for the early prediction of medical outcomes. Research on whether the use of clinical notes and concepts from knowledge bases can improve the performance of prediction...
The Liver Intensive Care Unit.
Clinics in liver disease
Jan 30, 2025
Major advances in managing critically ill patients with liver disease have improved their prognosis and access to intensive care facilities. Acute-on-chronic liver failure (ACLF) is now a well-defined disease and these patients can be fast-tracked fo...
Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit.
Current cardiology reports
Jan 20, 2025
PURPOSE OF REVIEW: Artificial Intelligence (AI) technology will significantly alter critical care cardiology, from our understanding of diseases to the way in which we communicate with patients and colleagues. We summarize the potential applications ...
AI Interventions to Alleviate Healthcare Shortages and Enhance Work Conditions in Critical Care: Qualitative Analysis.
Journal of medical Internet research
Jan 13, 2025
BACKGROUND: The escalating global scarcity of skilled health care professionals is a critical concern, further exacerbated by rising stress levels and clinician burnout rates. Artificial intelligence (AI) has surfaced as a potential resource to allev...
Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.
BMC medical informatics and decision making
Dec 18, 2024
BACKGROUND: Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate identification of BT needs can enhance patient outcomes and healthcare resource management.
Machine learning outperforms the Canadian Triage and Acuity Scale (CTAS) in predicting need for early critical care.
CJEM
Nov 19, 2024
STUDY OBJECTIVE: This study investigates the potential to improve emergency department (ED) triage using machine learning models by comparing their predictive performance with the Canadian Triage Acuity Scale (CTAS) in identifying the need for critic...
Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.
Scientific reports
Nov 8, 2024
Given the limited capacity to accurately determine the necessity for intubation in intensive care unit settings, this study aimed to develop and externally validate an interpretable machine learning model capable of predicting the need for intubation...
Advancing Neurocritical Care with Artificial Intelligence and Machine Learning: The Promise, Practicalities, and Pitfalls ahead.
Neurologic clinics
Oct 15, 2024
Expansion of artificial intelligence (AI) in the field of medicine is changing the paradigm of clinical practice at a rapid pace. Incorporation of AI in medicine offers new tools as well as challenges, and physicians and learners need to adapt to ass...
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