Machine learning in cardiovascular medicine: are we there yet?

Abstract

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.

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“…This means machine learning techniques can explore the structure of the data, in terms of associations between the variables, without having a theory of how the structure looks like. This might make them better suited to detect associations between variables than logistic or Cox regression [11]. However, as discussed by Pellegrini et al [8], in a published systematic literature and meta-analyses of machine learning techniques in neuroimaging for cognitive impairment and dementia, studies using machine learning algorithms also show limitations.…”

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“…However, we prospectively used the cross-validation approach to validate our findings, as recommended. [41, 45] Finally, Gleason scores were available in a very small proportion of the patients, but might otherwise improve prediction accuracy. [46]…”

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