Synapse
⌘+K
Synapse
PulseExploreJournal ClubResearchersJournals
Instagram
HomeJournal ClubExplore
August 22, 2025Journal of the American Heart AssociationOpen Access

Using Machine Learning to Risk Stratify Emergency Department Patients With Chest Pain but No Acute Myocardial Infarction: A Multicenter Retrospective Analysis

View Full Paper
Ask AI
Bookmark
Share

Authors

ECEric ChouTLTsung‐Chien LuYCYu Wei Chiu

Discussion

Loading...

Member takes

Overview

Retrospective analysis identifies risk factors for major adverse cardiac events in ED patients, implying improved prediction using machine learning.

Key Points

  • The long short-term memory model accurately predicts 30-day major adverse cardiac events in emergency department patients, enhancing risk assessment.
  • In this analysis of 14,177 patients, 39 experienced 30-day major adverse cardiac events, showing a significant predictive performance.
  • This retrospective analysis focused on patients with chest pain, excluding those diagnosed with acute myocardial infarction during the visit.
  • The findings support the use of a machine learning model to predict cardiac risk, potentially improving emergency care outcomes.

Cite This Study

Chou et al. (2025) studied this question.

synapsesocial.com/papers/68af76b27567bf4f94fef5dbhttps://doi.org/10.1161/jaha.125.041915
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Risk stratification of chest pain in the emergency department using artificial intelligence applied to electrocardiograms2025
  2. 2Improving chest pain risk assessment: validation of HEART, TIMI, GRACE, EDACS-ADP, and HET for MACE prediction in the emergency department2025 · 7 citations
  3. 3Risk Prediction of Major Adverse Cardiovascular Events Within One Year After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome: Machine Learning–Based Time-to-Event Analysis2025
  4. 4Comparison of the Prognostic Performance of Various Machine Learning Models in Patients with Acute Myocardial Infarction: Results from the COREA-AMI Registry2025
  5. 5Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification2025