Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk

Hanyin Wang, Yikuan Li, Hongyan Ning, John T Wilkins, Donald M Lloyd-Jones, Yuan Luo*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cardiovascular disease is prevalent and associated with significant mortality rate. Robust lifetime risk stratification for cardiovascular disease is important for effective prevention, early diagnoses, targeted intervention, and improved prognosis. Health disparities, manifested as socio-behavioral factors, are believed to have multiple effects throughout life with great complexity. Multiple studies investigated lifetime cardiovascular-related mortality risk prediction focusing on subjects' pathophysiology and intervention profiles. In this study, we applied machine learning algorithms and focused on integrating socio-behavioral factors to pathophysiology and intervention profiles to predict cardiovascular-related mortality risk. Our results showed that multiple machine learning algorithms can predict risk with reasonable accuracy, using mixed types of features. Particularly, socio-behavioral factors contributed significantly to the improved accuracy of mortality risk prediction. Feature analysis identified the odds ratio of socio-behavioral factors for cardiovascular-related mortality and offered potential insights on how they impact subjects' long-term outcomes. Our results call for further investigation of this important topic.

Original languageEnglish (US)
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Pages433-437
Number of pages5
ISBN (Electronic)9781643680026
DOIs
StatePublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in Health Technology and Informatics
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019
CountryFrance
CityLyon
Period8/25/198/30/19

Fingerprint

Learning systems
Mortality
Learning algorithms
Cardiovascular Diseases
Early Diagnosis
Odds Ratio
Machine Learning
Health

Keywords

  • Cardiovascular Diseases
  • Machine Learning
  • Risk Factors

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Wang, H., Li, Y., Ning, H., Wilkins, J. T., Lloyd-Jones, D. M., & Luo, Y. (2019). Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 433-437). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190258
Wang, Hanyin ; Li, Yikuan ; Ning, Hongyan ; Wilkins, John T ; Lloyd-Jones, Donald M ; Luo, Yuan. / Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Brigitte Seroussi ; Lucila Ohno-Machado ; Lucila Ohno-Machado ; Brigitte Seroussi. IOS Press, 2019. pp. 433-437 (Studies in Health Technology and Informatics).
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abstract = "Cardiovascular disease is prevalent and associated with significant mortality rate. Robust lifetime risk stratification for cardiovascular disease is important for effective prevention, early diagnoses, targeted intervention, and improved prognosis. Health disparities, manifested as socio-behavioral factors, are believed to have multiple effects throughout life with great complexity. Multiple studies investigated lifetime cardiovascular-related mortality risk prediction focusing on subjects' pathophysiology and intervention profiles. In this study, we applied machine learning algorithms and focused on integrating socio-behavioral factors to pathophysiology and intervention profiles to predict cardiovascular-related mortality risk. Our results showed that multiple machine learning algorithms can predict risk with reasonable accuracy, using mixed types of features. Particularly, socio-behavioral factors contributed significantly to the improved accuracy of mortality risk prediction. Feature analysis identified the odds ratio of socio-behavioral factors for cardiovascular-related mortality and offered potential insights on how they impact subjects' long-term outcomes. Our results call for further investigation of this important topic.",
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Wang, H, Li, Y, Ning, H, Wilkins, JT, Lloyd-Jones, DM & Luo, Y 2019, Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk. in B Seroussi, L Ohno-Machado, L Ohno-Machado & B Seroussi (eds), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 264, IOS Press, pp. 433-437, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, France, 8/25/19. https://doi.org/10.3233/SHTI190258

Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk. / Wang, Hanyin; Li, Yikuan; Ning, Hongyan; Wilkins, John T; Lloyd-Jones, Donald M; Luo, Yuan.

MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Brigitte Seroussi; Lucila Ohno-Machado; Lucila Ohno-Machado; Brigitte Seroussi. IOS Press, 2019. p. 433-437 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk

AU - Wang, Hanyin

AU - Li, Yikuan

AU - Ning, Hongyan

AU - Wilkins, John T

AU - Lloyd-Jones, Donald M

AU - Luo, Yuan

PY - 2019/8/21

Y1 - 2019/8/21

N2 - Cardiovascular disease is prevalent and associated with significant mortality rate. Robust lifetime risk stratification for cardiovascular disease is important for effective prevention, early diagnoses, targeted intervention, and improved prognosis. Health disparities, manifested as socio-behavioral factors, are believed to have multiple effects throughout life with great complexity. Multiple studies investigated lifetime cardiovascular-related mortality risk prediction focusing on subjects' pathophysiology and intervention profiles. In this study, we applied machine learning algorithms and focused on integrating socio-behavioral factors to pathophysiology and intervention profiles to predict cardiovascular-related mortality risk. Our results showed that multiple machine learning algorithms can predict risk with reasonable accuracy, using mixed types of features. Particularly, socio-behavioral factors contributed significantly to the improved accuracy of mortality risk prediction. Feature analysis identified the odds ratio of socio-behavioral factors for cardiovascular-related mortality and offered potential insights on how they impact subjects' long-term outcomes. Our results call for further investigation of this important topic.

AB - Cardiovascular disease is prevalent and associated with significant mortality rate. Robust lifetime risk stratification for cardiovascular disease is important for effective prevention, early diagnoses, targeted intervention, and improved prognosis. Health disparities, manifested as socio-behavioral factors, are believed to have multiple effects throughout life with great complexity. Multiple studies investigated lifetime cardiovascular-related mortality risk prediction focusing on subjects' pathophysiology and intervention profiles. In this study, we applied machine learning algorithms and focused on integrating socio-behavioral factors to pathophysiology and intervention profiles to predict cardiovascular-related mortality risk. Our results showed that multiple machine learning algorithms can predict risk with reasonable accuracy, using mixed types of features. Particularly, socio-behavioral factors contributed significantly to the improved accuracy of mortality risk prediction. Feature analysis identified the odds ratio of socio-behavioral factors for cardiovascular-related mortality and offered potential insights on how they impact subjects' long-term outcomes. Our results call for further investigation of this important topic.

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KW - Risk Factors

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PB - IOS Press

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Wang H, Li Y, Ning H, Wilkins JT, Lloyd-Jones DM, Luo Y. Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk. In Seroussi B, Ohno-Machado L, Ohno-Machado L, Seroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. IOS Press. 2019. p. 433-437. (Studies in Health Technology and Informatics). https://doi.org/10.3233/SHTI190258