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Heart. 2018 Jan 19. pii: heartjnl-2017-311198. doi: 10.1136/heartjnl-2017-311198. [Epub ahead of print]

Machine learning in cardiovascular medicine: are we there yet?

Shameer K1,2,3,4,5,6, Johnson KW2,3,4,5, Glicksberg BS2,3,4,5,7, Dudley JT2,3,4,5, Sengupta PP8.

Author information

1
Departments of Medical Informatics and Research Informatics, Northwell Health, Great Neck, New York, USA.
2
Institute for Next Generation Healthcare, Mount Sinai Health System, New York City, New York, USA.
3
Icahn Institute for Genomics and Multiscale Biology, Mount Sinai Health System, New York City, New York, USA.
4
Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York City, New York, USA.
5
Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA.
6
Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA.
7
Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, California, USA.
8
Division of Cardiology, West Virginia Heart and Vascular Institute, Morgantown, West Virginia, USA.

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.

KEYWORDS:

heart disease

Conflict of interest statement

Competing interests: PPS is a consultant for TeleHealthRobotics, HeartSciences and Hitachi-Aloka. JTD has received consulting fees or honoraria from Janssen Pharmaceuticals, GlaxoSmithKline, AstraZeneca and Hoffman-La Roche. JTD is a scientific advisor to LAM Therapeutics and holds equity in NuMedii, Ayasdi and Ontomics. KS has received consulting fees or honoraria from Philips Healthcare, Google, LEK Consulting and Parthenon-EY. All other authors declare no competing interests.

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