Cardiovascular Risk Prediction Using Machine Learning in a Large Japanese Cohort-Reference-Cited by-同舟云学术

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Cardiovascular Risk Prediction Using Machine Learning in a Large Japanese Cohort

Published:2022-12-09 Issue:12 Volume:4 Page:595-603
ISSN:2434-0790
Container-title:Circulation Reports
language:en
Short-container-title:Circ Rep

Author:

Matheson Matthew B.¹,Kato Yoko²,Baba Shinichi³,Cox Christopher¹,Lima João A.C.²,Ambale-Venkatesh Bharath⁴

Affiliation:

1. Johns Hopkins Bloomberg School of Public Health

2. Department of Cardiology, Johns Hopkins Hospital and School of Medicine

3. Toshiba Corporate Research and Development Center

4. Department of Radiology, Johns Hopkins Hospital and School of Medicine

Publisher

Japanese Circulation Society

Subject

Marketing,Organizational Behavior and Human Resource Management,Strategy and Management,Drug Discovery,Pharmaceutical Science,Pharmacology

Link

https://www.jstage.jst.go.jp/article/circrep/4/12/4_CR-22-0101/_pdf

Reference26 articles.

1. 1. Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998; 97: 1837–1847, doi:10.1161/01.cir.97.18.183.

2. 2. Goff DC, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk. Circulation 2014; 129(25_suppl_2): S49–S73, doi:10.1161/01.cir.0000437741.48606.98.

3. 3. Goldstein BA, Navar AM, Carter RE. Moving beyond regression techniques in cardiovascular risk prediction: Applying machine learning to address analytic challenges. Eur Heart J 2017; 38: 1805–1814, doi:10.1093/eurheartj/ehw302.

4. 4. Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: Are we there yet? Heart 2018; 104: 1156–1164, doi:10.1136/heartjnl-2017-311198.

5. 5. Alaa AM, Bolton T, Angelantonio ED, Rudd JHF, van der Schaar M. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants. PLoS One 2019; 14: e0213653, doi:10.1371/journal.pone.0213653.

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3. Unveiling Coronary Heart Disease Prediction through Machine Learning Techniques: Insights from the Suita Population-Based Cohort Study;2024-04-02

4. Machine learning analysis and risk prediction of weather-sensitive mortality related to cardiovascular disease during summer in Tokyo, Japan;Scientific Reports;2023-10-09

5. POSSIBILITIES OF APPLYING MACHINE LEARNING TECHNOLOGIES IN THE SPHERE OF PRIMARY PREVENTION OF CARDIOVASCULAR DISEASES;Complex Issues of Cardiovascular Diseases;2023-09-25

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