Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease

IF 5.8 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Canadian Journal of Cardiology Pub Date : 2024-10-01 DOI:10.1016/j.cjca.2024.07.026
Bhargava K. Chinni MS , Cedric Manlhiot PhD
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Abstract

Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient’s condition, personalized medicine relies on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often generated through machine-learning algorithms. There are a number of analytic considerations regarding the creation of digital biomarkers that are discussed in this review, including data preprocessing, time dependency and gating, dimensionality reduction, and novel methods, both in the realm of supervised and unsupervised machine learning. Some of these considerations, such as sample size requirements and measurements of model performance, are particularly challenging in small and heterogeneous populations with rare outcomes such as children with congenital heart disease. Finally, we review analytic considerations for the deployment of digital biomarkers in clinical settings, including the emerging field of clinical artificial intelligence (AI) operations, computational needs for deployment, efforts to increase the explainability of AI, algorithmic drift, and the needs for distributed surveillance and federated learning. We conclude this review by discussing a recent simulation study that shows that, despite these analytic challenges and complications, the use of digital biomarkers in managing clinical care might have substantial benefits regarding individual patient outcomes.
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利用机器学习对儿科和先天性心脏病进行个性化医疗的新兴分析方法。
尽管精准医疗和个性化医疗仍是一个新兴领域,但如今已成为心脏病专家耳熟能详的术语。精准医疗通常依赖于患者病情的潜在生物学/病理生理学,而个性化医疗则依赖于通过算法生成的数字生物标记。鉴于基础数据的复杂性,这些数字生物标志物通常是通过机器学习算法生成的。本综述将讨论创建数字生物标志物的一系列分析考虑因素,包括:数据预处理、时间依赖性和门控、降维以及有监督和无监督机器学习领域的新方法。其中一些考虑因素,如样本量要求和模型性能测量,对于像先天性心脏病患儿这样具有罕见结果的小规模异质性人群尤其具有挑战性。最后,我们回顾了在临床环境中部署数字生物标记的分析考虑因素,包括新兴的临床人工智能操作领域、部署的计算需求、提高人工智能可解释性的努力、算法漂移以及分布式监控和联合学习的需求。在本综述的最后,我们讨论了最近的一项模拟研究,该研究表明,尽管存在这些分析挑战和并发症,但在临床护理管理中使用数字生物标记物可能会对个体患者的预后产生巨大的益处。
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来源期刊
Canadian Journal of Cardiology
Canadian Journal of Cardiology 医学-心血管系统
CiteScore
9.20
自引率
8.10%
发文量
546
审稿时长
32 days
期刊介绍: The Canadian Journal of Cardiology (CJC) is the official journal of the Canadian Cardiovascular Society (CCS). The CJC is a vehicle for the international dissemination of new knowledge in cardiology and cardiovascular science, particularly serving as the major venue for Canadian cardiovascular medicine.
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