AIMC Topic: Heart Arrest
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System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method.
Neurocritical care
Feb 20, 2025
BACKGROUND: A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests usin...
Use of machine learning models to identify National Institutes of Health-funded cardiac arrest research.
Resuscitation
Feb 15, 2025
OBJECTIVE: To compare the performance of three artificial intelligence (AI) classification strategies against manually classified National Institutes of Health (NIH) cardiac arrest (CA) grants, with the goal of developing a publicly available tool to...
A deep learning model for QRS delineation in organized rhythms during in-hospital cardiac arrest.
International journal of medical informatics
Jan 30, 2025
BACKGROUND: Cardiac arrest (CA) is the sudden cessation of heart function, typically resulting in loss of consciousness and cessation of pulse and breathing. The electrocardiogram (ECG) stands as an essential tool extensively utilized by clinicians, ...
Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment.
Journal of translational medicine
Jan 6, 2025
INTRODUCTION: Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment de...
A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.
Annals of laboratory medicine
Dec 13, 2024
BACKGROUND: Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles...
A randomized controlled trial on evaluating clinician-supervised generative AI for decision support.
International journal of medical informatics
Nov 29, 2024
BACKGROUND: The integration of generative artificial intelligence (AI) as clinical decision support systems (CDSS) into telemedicine presents a significant opportunity to enhance clinical outcomes, yet its application remains underexplored.
Prediction of Survival After Pediatric Cardiac Arrest Using Quantitative EEG and Machine Learning Techniques.
Neurology
Nov 20, 2024
BACKGROUND AND OBJECTIVES: Early neuroprognostication in children with reduced consciousness after cardiac arrest (CA) is a major clinical challenge. EEG is frequently used for neuroprognostication in adults, but has not been sufficiently validated f...
Post-Cardiac arrest outcome prediction using machine learning: A systematic review and meta-analysis.
International journal of medical informatics
Oct 28, 2024
BACKGROUND: Early and reliable prognostication in post-cardiac arrest patients remains challenging, with various factors linked to return of spontaneous circulation (ROSC), survival, and neurological results. Machine learning and deep learning models...
Prediction model of in-hospital cardiac arrest using machine learning in the early phase of hospitalization.
The Kaohsiung journal of medical sciences
Sep 25, 2024
In hospitals, the deterioration of a patient's condition leading to death is often preceded by physiological abnormalities in the hours to days beforehand. Several risk-scoring systems have been developed to identify patients at risk of major adverse...
Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.
Journal of medical Internet research
Sep 17, 2024
BACKGROUND: Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been...
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