ArticleCureus2026
Automated ECG Report as a Factor in the Clinical Decision Pathway for Acute Chest Pain in the Emergency Department.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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Abstract
Background Electrocardiographic analysis algorithms have consistently evolved, becoming essential tools for physicians in diverse settings, particularly in assessing patients with acute chest pain. Moving forward, it is crucial to classify unstructured automated ECG reports into clinically relevant outcomes using advanced large language models. This approach holds significant potential to enhance an accelerated clinical decision pathway in clinical settings. Objective This study aims to integrate automated electrocardiogram algorithms with advanced machine learning techniques, enhancing the classification of ECG reports within emergency department settings. Specifically, it investigates how natural language processing can augment traditional methods to accelerate the electrocardiographic-directed management of acute chest pain. Methods Employing a retrospective observational dataset from Rashid Hospital, Dubai, spanning from June 2022 to August 2022, we analyzed 860 ECGs from patients presenting with acute chest pain. The ECGs were categorized into four classes, namely, STEMI, NSTEMI, normal ECG, and new arrhythmia using a hybrid model that combines the established Glasgow algorithm with a large language model, GPT-4. The Glasgow algorithm produced structured text inputs, which were then classified by GPT-4 using few-shot prompting (temperature = 0.2, top_p=1.0). Results The model demonstrates high predictive accuracy for normal ECGs, achieving an F1 score of 0.93, followed by STEMI with an F1 score of 0.80. New arrhythmias, however, present more challenges, reflected by the lowest F1 score of 0.45. Notably, the model excels in discriminating between STEMI and normal ECGs (AUC=0.92) and between STEMI and new arrhythmias (AUC=0.91). Overall accuracy was 85.9% (95% CI: 0.816-0.895) Conclusion The findings suggest that leveraging deep learning alongside traditional algorithms can significantly improve the rapid classification of ECGs, supporting accelerated decision-making pathways in clinical practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.