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A Multimodal Machine Learning Approach to Omics-Based Risk Stratification in Coronary Artery Disease

  • Eleni I. GeorgaEmail author
  • Nikolaos S. Tachos
  • Antonis I. Sakellarios
  • Gualtiero Pelosi
  • Silvia Rocchiccioli
  • Oberdan Parodi
  • Lampros K. Michalis
  • Dimitrios I. Fotiadis
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 68/3)

Abstract

This study aims at developing a personalized model for coronary artery disease (CAD) risk stratification based on machine learning modelling of non-imaging data, i.e. clinical, molecular, cellular, inflammatory, and omics data. A multimodal architectural approach is proposed whose generalization capability, with respect to CAD stratification, is currently evaluated. Different data fusion techniques are investigated, ranging from early to late integration methods, aiming at designing a predictive model capable of representing genotype-phenotype interactions pertaining to CAD development. An initial evaluation of the discriminative capacity of the feature space with respect to a binary classification problem (No CAD, CAD), although not complete, shows that: (i) kernel-based classification provides more accurate results as compared with neural network-based and decision tree-based modelling, and (ii) appropriate input refinement by feature ranking has the potential to increase the sensitivity of the model.

Keywords

Coronary artery disease Patient risk stratification Multi-modal machine learning 

Notes

Acknowledgements

This work is funded by the European Commission: Project SMARTOOL, “Simulation Modeling of coronary ARTery disease: a tool for clinical decision support—SMARTool” GA number: 689068.

Compliance with Ethical Standards

Conflict of Interest: The authors declare that they have no conflict of interest.

Ethical approval: “All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.”

Informed consent: “Informed consent was obtained from all individual participants included in the study.”

References

  1. 1.
    Stone, P.H., et al., Prediction of progression of coronary artery disease and clinical outcomes using vascular profiling of endothelial shear stress and arterial plaque characteristics: the PREDICTION Study. Circulation, 2012. 126(2): p. 172–81.Google Scholar
  2. 2.
    Sakellarios, A., et al., Prediction of atherosclerotic disease progression using LDL transport modelling: a serial computed tomographic coronary angiographic study. European Heart Journal: Cardiovascular Imaging, 2017. 18(1): p. 11–18.Google Scholar
  3. 3.
    D’Agostino, R.B., Sr., et al., General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation, 2008. 117(6): p. 743–53.Google Scholar
  4. 4.
    Conroy, R.M., et al., Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J, 2003. 24(11): p. 987–1003.Google Scholar
  5. 5.
    Hippisley-Cox, J., et al., Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ, 2010. 341: p. c6624.Google Scholar
  6. 6.
    Damen, J.A., et al., Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 2016. 353: p. i2416.Google Scholar
  7. 7.
    Weng, S.F., et al., Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 2017. 12(4): p. e0174944.Google Scholar
  8. 8.
    Choi, E., et al., Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association: JAMIA, 2017. 24(2): p. 361–370.Google Scholar
  9. 9.
    Motwani, M., et al., Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. European Heart Journal, 2017. 38(7): p. 500–507.Google Scholar
  10. 10.
    Goldstein, B.A., A.M. Navar, and R.E. Carter, Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. European Heart Journal, 2017. 38(23): p. 1805–1814.Google Scholar
  11. 11.
    Rumsfeld, J.S., K.E. Joynt, and T.M. Maddox, Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol, 2016. 13(6): p. 350–9.Google Scholar
  12. 12.
    Groeneveld, P.W. and J.S. Rumsfeld, Can Big Data Fulfill Its Promise? Circ Cardiovasc Qual Outcomes, 2016. 9(6): p. 679–682.Google Scholar
  13. 13.
    Li, Y., F.X. Wu, and A. Ngom, A review on machine learning principles for multi-view biological data integration. Brief Bioinform, 2016.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Eleni I. Georga
    • 1
    Email author
  • Nikolaos S. Tachos
    • 2
  • Antonis I. Sakellarios
    • 2
  • Gualtiero Pelosi
    • 3
  • Silvia Rocchiccioli
    • 3
  • Oberdan Parodi
    • 3
  • Lampros K. Michalis
    • 4
  • Dimitrios I. Fotiadis
    • 1
    • 2
  1. 1.Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and EngineeringUniversity of IoanninaIoanninaGreece
  2. 2.Department of Biomedical ResearchInstitute of Molecular Biology and Biotechnology, Foundation for Research and Technology–Hellas (FORTH)IoanninaGreece
  3. 3.Institute of Clinical Physiology, National Research CouncilPisaItaly
  4. 4.Department of Cardiology, Medical SchoolUniversity of IoanninaIoanninaGreece

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