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

doi:10.3233/SHTI190258

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 proceeding › Conference 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 language	English (US)
Title of host publication	MEDINFO 2019
Subtitle of host publication	Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
Editors	Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
Publisher	IOS Press
Pages	433-437
Number of pages	5
ISBN (Electronic)	9781643680026
DOIs	https://doi.org/10.3233/SHTI190258
State	Published - Aug 21 2019
Event	17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: Aug 25 2019 → Aug 30 2019

Publication series

Name	Studies in Health Technology and Informatics
Volume	264
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	17th World Congress on Medical and Health Informatics, MEDINFO 2019
Country	France
City	Lyon
Period	8/25/19 → 8/30/19

Keywords

Cardiovascular Diseases
Machine Learning
Risk Factors

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/SHTI190258

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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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title = "Using machine learning to integrate socio-behavioral factors in predicting cardiovascular-related mortality risk",

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.",

keywords = "Cardiovascular Diseases, Machine Learning, Risk Factors",

author = "Hanyin Wang and Yikuan Li and Hongyan Ning and Wilkins, {John T} and Lloyd-Jones, {Donald M} and Yuan Luo",

note = "Funding Information: This work was supported by NIH Grant 1R21LM012618. We thank Ying Liu at UT Dallas for helpful comment.; 17th World Congress on Medical and Health Informatics, MEDINFO 2019 ; Conference date: 25-08-2019 Through 30-08-2019",

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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 proceeding › Conference contribution

TY - GEN

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

N1 - Funding Information: This work was supported by NIH Grant 1R21LM012618. We thank Ying Liu at UT Dallas for helpful comment.

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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A2 - Seroussi, Brigitte

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Y2 - 25 August 2019 through 30 August 2019

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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