真实世界心血管疾病研究中的机器学习方法

IF 0.9 4区 医学 Q4 CARDIAC & CARDIOVASCULAR SYSTEMS Cardiovascular Innovations and Applications Pub Date : 2023-03-24 DOI:10.15212/cvia.2023.0011
Jiawei Zhou, Dongfang You, Jianling Bai, Xin Chen, Yaqian Wu, Zhongtian Wang, Yingdan Tang, Yang Zhao, G. Feng
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引用次数: 0

摘要

目的:心血管疾病(CVD)是世界范围内的主要死亡原因之一,在许多方面,特别是风险识别和预后预测方面,迫切需要答案。具有大量观测结果的现实世界研究为CVD研究提供了重要的基础,但受高维、缺失或非结构化数据的限制。机器学习(ML)方法,包括各种监督和无监督算法,对数据治理很有用,并且对现实世界研究中的高维数据分析和输入有效。本文综述了几种常用的机器学习方法在CVD领域的理论、优缺点及应用,为进一步应用提供参考。方法:本文介绍了层次聚类、k-means聚类、主成分分析、随机森林、支持向量机、神经网络等多种常用机器学习算法的起源、目的、理论、优势和局限性,以及应用。一个例子是在收缩压干预试验(SPRINT)数据上使用随机森林来演示ML在心血管疾病中的应用过程和主要结果。结论:ML方法是产生真实世界证据以支持临床决策和满足临床需要的有效工具。本文以通俗易懂的语言阐述了多种机器学习方法的原理,为进一步的应用提供参考。未来的研究需要开发准确的集成学习方法,以便在医学领域得到广泛的应用。
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Machine Learning Methods in Real-World Studies of Cardiovascular Disease
Objective: Cardiovascular disease (CVD) is one of the leading causes of death worldwide, and answers are urgently needed regarding many aspects, particularly risk identification and prognosis prediction. Real-world studies with large numbers of observations provide an important basis for CVD research but are constrained by high dimensionality, and missing or unstructured data. Machine learning (ML) methods, including a variety of supervised and unsupervised algorithms, are useful for data governance, and are effective for high dimensional data analysis and imputation in real-world studies. This article reviews the theory, strengths and limitations, and applications of several commonly used ML methods in the CVD field, to provide a reference for further application. Methods: This article introduces the origin, purpose, theory, advantages and limitations, and applications of multiple commonly used ML algorithms, including hierarchical and k-means clustering, principal component analysis, random forest, support vector machine, and neural networks. An example uses a random forest on the Systolic Blood Pressure Intervention Trial (SPRINT) data to demonstrate the process and main results of ML application in CVD. Conclusion: ML methods are effective tools for producing real-world evidence to support clinical decisions and meet clinical needs. This review explains the principles of multiple ML methods in plain language, to provide a reference for further application. Future research is warranted to develop accurate ensemble learning methods for wide application in the medical field.
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来源期刊
Cardiovascular Innovations and Applications
Cardiovascular Innovations and Applications CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
0.80
自引率
20.00%
发文量
222
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