AIMC Topic: Heart Arrest
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Predicting 30-day survival after in-hospital cardiac arrest: a nationwide cohort study using machine learning and SHAP analysis.
BMJ open
Apr 27, 2025
OBJECTIVE: In-hospital cardiac arrest (IHCA) presents a critical challenge with low survival rates and limited prediction tools. Despite advances in resuscitation, predicting 30-day survival remains difficult, and current methods lack interpretabilit...
Construction and validation of prognostic model for ICU mortality in cardiac arrest patients: an interpretable machine learning modeling approach.
European journal of medical research
Apr 24, 2025
BACKGROUND: The incidence and mortality of cardiac arrest (CA) is high. We developed interpretable machine learning models for early prediction of ICU mortality risk in patients diagnosed with CA.
Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.
Scientific reports
Apr 3, 2025
Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making. Existing studies have predominantly focused on EEG for assessing brain injury, with some exploring...
Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.
Nature medicine
Apr 2, 2025
The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pr...
False Crisis Alarms in Cardiopulmonary Monitoring:: Identification, Causes, and Clinical Implications.
Critical care nursing clinics of North America
Mar 26, 2025
The systematic annotation of crisis alarms reveals a high number of false alarms for both ventricular tachycardia and asystole, which are best identified by inspecting simultaneous multilead electrocardiographs. Among the few true crisis alarms, 11 w...
Eye movement detection using electrooculography and machine learning in cardiac arrest patients.
Resuscitation
Mar 24, 2025
AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. ...
Eligibility for eCPR Warming in Hypothermic Cardiac Arrest: Lack of Guidelines and the Current Constraints of Artificial Intelligence in Clinical Decision-Making.
Artificial organs
Mar 13, 2025
AIM OF THE STUDY: Artificial intelligence (AI) such as large language models (LLMs) tools are potential sources of information on hypothermic cardiac arrest (HCA). The aim of our study was to determine whether, for patients with HCA, LLMs provide inf...
Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.
Scientific reports
Mar 13, 2025
Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 7...
Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis.
Journal of medical Internet research
Mar 10, 2025
BACKGROUND: Currently, there is a lack of effective early assessment tools for predicting the onset and development of cardiac arrest (CA). With the increasing attention of clinical researchers on machine learning (ML), some researchers have develope...
Deep Learning-Based Electrocardiogram Model (EIANet) to Predict Emergency Department Cardiac Arrest: Development and External Validation Study.
Journal of medical Internet research
Feb 28, 2025
BACKGROUND: In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency depart...
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