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06.12.2024 | Review

Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management

verfasst von: Shanshan Nie, Shan Zhang, Yuhang Zhao, Xun Li, Huaming Xu, Yongxia Wang, Xinlu Wang, Mingjun Zhu

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Abstract

Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is crucial for improved management and prognosis of patients. The rapid growth of machine learning (ML) research has significantly enhanced our understanding of ACS. Most studies have focused on applying ML to detect ACS, predict prognosis, manage treatment, identify risk factors, and discover potential biomarkers, particularly using data from electrocardiograms (ECGs), electronic medical records (EMRs), imaging, and omics as the main data modality. Additionally, integrating ML with smart devices such as wearables, smartphones, and sensor technology enables real-time dynamic assessments, enhancing clinical care for patients with ACS. This review provided an overview of the workflow and key concepts of ML as they relate to ACS. It then provides an overview of current ML algorithms used for ACS diagnosis, prognosis, identification of potential risk biomarkers, and management. Furthermore, we discuss the current challenges faced by ML algorithms in this field and how they might be addressed in the future, especially in the context of medicine.

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Titel: Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management
verfasst von: Shanshan Nie
Shan Zhang
Yuhang Zhao
Xun Li
Huaming Xu
Yongxia Wang
Xinlu Wang
Mingjun Zhu
Publikationsdatum: 06.12.2024
Verlag: Springer Healthcare
Erschienen in: Advances in Therapy
Print ISSN: 0741-238X
Elektronische ISSN: 1865-8652
DOI: https://doi.org/10.1007/s12325-024-03060-z

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