Departments of Medical Informatics and Research Informatics, Northwell Health, Great Neck, New York, USA.
Institute for Next Generation Healthcare, Mount Sinai Health System, New York City, New York, USA.
Heart. 2018 Jul;104(14):1156-1164. doi: 10.1136/heartjnl-2017-311198. Epub 2018 Jan 19.
Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.
人工智能（AI）泛指从数据中迭代学习的分析算法，使计算机能够在无需明确编程寻找目标的情况下发现隐藏的见解。这包括一系列操作，涵盖了机器学习、认知学习、深度学习和强化学习等术语，可用于整合和解释传统统计方法可能无法执行的复杂生物医学和医疗保健数据。在这篇综述文章中，我们讨论了机器学习算法的基础知识以及存在哪些潜在的数据来源；评估了对机器学习的需求；并考察了在心血管医学领域实施机器学习的潜在局限性和挑战。人工智能在医学领域最有前途的应用途径是开发可用于指导临床护理的自动化风险预测算法；使用无监督学习技术更精确地表现复杂疾病；以及实施强化学习算法，以智能地增强医疗保健提供者。基于机器学习的预测模型的实用性将取决于包括数据异质性、数据深度、数据广度、建模任务的性质、机器学习和特征选择算法的选择以及正交证据等因素。批判性地理解各种适用于机器学习的方法和任务的优缺点至关重要。通过利用医学领域日益增长的大数据，我们详细阐述了机器学习可能促进特定于患者的模型的最佳发展的途径，从而改善心血管医学中的诊断、干预和结果。
