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Machine learning in cardiovascular medicine: are we there yet?
  1. Khader Shameer1,2,3,4,5,6,
  2. Kipp W Johnson2,3,4,5,
  3. Benjamin S Glicksberg2,3,4,5,7,
  4. Joel T Dudley2,3,4,5,
  5. Partho P Sengupta8
  1. 1Departments of Medical Informatics and Research Informatics, Northwell Health, Great Neck, New York, USA
  2. 2Institute for Next Generation Healthcare, Mount Sinai Health System, New York City, New York, USA
  3. 3Icahn Institute for Genomics and Multiscale Biology, Mount Sinai Health System, New York City, New York, USA
  4. 4Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York City, New York, USA
  5. 5Icahn School of Medicine at Mount Sinai, Mount Sinai Health System, New York City, New York, USA
  6. 6Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA
  7. 7Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, California, USA
  8. 8Division of Cardiology, West Virginia Heart and Vascular Institute, Morgantown, West Virginia, USA
  1. Correspondence to Dr Partho P Sengupta, Heart & Vascular Institute, West Virginia University, Morgantown, WV 26506-8059, USA; Partho.sengupta{at}wvumedicine.org

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.

  • heart disease

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Footnotes

  • Contributors PPS and JTD planned the outline of the article. KS, KWJ and BSG conducted the empirical analyses and literature survey, and compiled the figures. All authors contributed to the manuscript writing and editing.

  • Funding KS, KWJ, BSG and JTD acknowledge support from the National Institutes of Health: National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK, grant number R01-DK098242-03); National Cancer Institute (NCI, grant number U54-CA189201-02); Illuminating the Druggable Genome (IDG); Knowledge Management Center sponsored by NIH Common Fund; National Center for Advancing Translational Sciences (NCATS, grant number UL1TR000067); and Clinical and Translational Science Awards (CTSA) grant.

  • 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.

  • Provenance and peer review Commissioned; externally peer reviewed.

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