The DOI matches its Crossref record.
Using Machine Learning to Integrate Socio-Behavioral Factors in Predicting Cardiovascular-Related Mortality Risk
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Multiple studies have now pointed out that if the data an ML model is trained on is biased this often yields bias in the predictions186,187.”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [187]
Citations
Cites
Still being checked.
Cited by
Sorted by citations
Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528
“Multiple studies have now pointed out that if the data an ML model is trained on is biased this often yields bias in the predictions 186,187 .”
BackgroundLi, Y., Wang, H., & Luo, Y. (2022) · Circulation Heart Failure · doi:10.1161/circheartfailure.122.009473 · Cited by 70
Cai, Y., Gong, D.-X., Tang, L.-Y., Cai, Y. H., Li, H., Jing, T.-C., Gong, M., Hu, W., Zhang, Z.-W., Zhang, X., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/47645 · Cited by 54
Wang, H., Li, Y., Naidech, A. M., & Luo, Y. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01871-0 · Cited by 28
do Nascimento, C. F., dos Santos, H. G., Batista, A. F. D. M., Lay, A. A. R., Duarte, Y. A. O., & Filho, A. D. P. C. (2021) · Age and Ageing · doi:10.1093/ageing/afab067 · Cited by 9
Liu, Y., Wang, Y., Zhao, Y., & Li, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13062 · Cited by 8
Snowdon, J. L., Scheufele, E. L., Pritts, J., Le, P., Mensah, G. A., Zhang, X., & Dankwa‐Mullan, I. (2023) · Ethnicity & Disease · doi:10.18865/1704 · Cited by 6
He, W., Ding, Y., Zhou, K., Chen, T., Wu, H., Shen, L., & Shang, T. (2024) · Europe PMC (PubMed Central) · doi:10.17615/n8gt-6715 · Cited by 5
Liu, S., & Luo, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09507 · Cited by 3
Liu, Y. (2024) · International Conference on New Media Development and Modernized Education (NMDME) · doi:10.4108/eai.13-10-2023.2341032 · Cited by 2
An early prediction of covid-19 associated hospitalization surge using deep learning approachPreprint
Meng, Y., Sun, Q., Hong, S., Zhao, Y., & Li, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.08093
do Nascimento, C. F. (2021) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.6.2021.tde-13102021-192542
Metadata
- Authors
- Hanyin, W., Yikuan, L., Hongyan, N., John, W., Donald, L.-J., & Yuan, L.
- Venue
- Studies in Health Technology and Informatics
- Publisher
- IOS Press
- Published
- 2019
- Volume
- 264
- Pages
- 433-437
- ISSN
- 0926-9630 (ISSN-L)
- OpenAlex
- W2978092503
- Cited by
- 13
- References
- 0
- Citations by year
- 2024: 3 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 3
- FWCI
- 5.64
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Studies in health technology and informatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- PubMed
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Keywords
- cardiovascular mortality, mortality prediction, socio-behavioral factors, health disparities, cardiovascular disease, pathophysiology, machine learning prediction models, odds ratio
- MeSH terms
- Machine Learning, Algorithms, Cardiovascular Diseases, Humans, Prognosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
