Advancements in the Early Identification and Treatment of Myocardial Infarction in the Emergency Department: A Comprehensive Review of Machine Learning Approaches - Suhail, Abdulrhman Ibrahim; Alnami, Ibrahim Abdulelah; Ibrahim, Rayan Ahmed; Al Anazi, Muhamad Radi Abdullah; Alhomidani, Hezam Fahad; Al Shiban, Hussain Ahmed Saleh; Almutairi, Fahad Owaidh Saeed; Aldhafeeri, Mater Mazi Munahi; Alharbi, Yousef Fraih Faisl (Journal of Ecohumanism, 2024)
Background: Early detection and prompt treatment of myocardial infarction (MI) in the emergency department (ED) are pivotal for reducing morbidity and mortality. Chest pain is a common presenting symptom in the ED, necessitating effective risk stratification and decision-making to distinguish between acute coronary syndromes (ACS) and benign conditions. Methods: This systematic review evaluates the application of machine learning (ML) algorithms in identifying myocardial infarction among patients presenting with nonspecific chest pain in the ED. A comprehensive search of databases including PubMed, Cochrane Library, and Embase was performed for studies published until 2023, which investigated ML methodologies in this context. Results: The review highlights a substantial interest in machine learning applications, demonstrating that ML techniques have significant potential to enhance diagnostic accuracy and prognostic capabilities compared to traditional clinical decision tools such as the TIMI and HEART scores. ML algorithms exhibited higher sensitivity and specificity in detecting MI, ultimately alleviating diagnostic burdens on emergency physicians. However, challenges remain in integrating these technologies into routine clinical practice due to issues related to data quality, model interpretability, and acceptance among healthcare providers. Conclusion: While machine learning holds promise for transforming the assessment of chest pain in the emergency department, further research is necessary to address existing limitations, including bias, data integration, and generalizability. The future landscape of emergency medicine could benefit from robust ML models that can assist clinicians in decision-making, leading to improved patient outcomes and more efficient healthcare delivery