Cited by 83Artificial Intelligence in Healthcare and EducationArtificial Intelligence in HealthcareMachine Learning in Healthcare

Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine

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Johnson, Kipp W., Shameer, Khader, Glicksberg, Benjamin S., Readhead, Ben, Sengupta, Partho P., Björkegren, Johan L.M., Kovacic, Jason C., Dudley, Joel T.·2017·JACC: Basic to Translational Science

Abstract

The traditional paradigm of cardiovascular disease research derives insight from large-scale, broadly inclusive clinical studies of well-characterized pathologies. These insights are then put into practice according to standardized clinical guidelines. However, stagnation in the development of new cardiovascular therapies and variability in therapeutic response implies that this paradigm is insufficient for reducing the cardiovascular disease burden. In this state-of-the-art review, we examine 3 interconnected ideas we put forth as key concepts for enabling a transition to precision cardiology: 1) precision characterization of cardiovascular disease with machine learning methods; 2) the application of network models of disease to embrace disease complexity; and 3) using insights from the previous 2 ideas to enable pharmacology and polypharmacology systems for more precise drug-to-patient matching and patient-disease stratification. We conclude by exploring the challenges of applying a precision approach to cardiology, which arise from a deficit of the required resources and infrastructure, and emerging evidence for the clinical effectiveness of this nascent approach.

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  • Statistical machine learning methods have been used to interrogate, model, and learn from complex multi-omics data to help address uncertainties about the connections between different types of data.

    “Statistical machine learning methods have also been used to interrogate, model, and learn from complex multi-omics data to help to address uncertainties about the connections between different types of data39.”

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Authors
Johnson, K. W., Shameer, K., Glicksberg, B. S., Readhead, B., Sengupta, P. P., Björkegren, J. L. M., Kovacic, J. C., & Dudley, J. T.
Affiliations
  • Mount Sinai Health System (US)
  • Icahn School of Medicine at Mount Sinai (US)
  • Karolinska Institutet (SE)
Corresponding author
Dudley, Joel T.
Venue
JACC: Basic to Translational Science
Publisher
Elsevier BV
Published
2017-06
Volume
2
Issue
3
Pages
311-327
ISSN
2452-302X (ISSN-L)
OpenAlex
W2679733416
Cited by
83
References
158
Citations by year
2026: 11 · 2025: 5 · 2024: 5 · 2023: 4 · 2022: 12 · 2021: 14 · 2020: 12 · 2019: 11 · 2018: 7 · 2017: 2
FWCI
0.24
Type
journal article
Language
en
Open access
Locations
  • JACC Basic to Translational Science (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • AstraZeneca
  • GlaxoSmithKline
  • F. Hoffmann-La Roche
  • Janssen Pharmaceuticals
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
precision cardiology, cardiovascular disease, machine learning, systems medicine, polypharmacology, patient stratification

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