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Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

Publication ,  Journal Article
Goldstein, BA; Navar, AM; Carter, RE
Published in: Eur Heart J
June 14, 2017

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning.

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Published In

Eur Heart J

DOI

EISSN

1522-9645

Publication Date

June 14, 2017

Volume

38

Issue

23

Start / End Page

1805 / 1814

Location

England

Related Subject Headings

  • Risk Factors
  • Risk Assessment
  • Regression Analysis
  • Precision Medicine
  • Models, Biological
  • Machine Learning
  • Humans
  • Cardiovascular System & Hematology
  • Cardiovascular Diseases
  • 3202 Clinical sciences
 

Citation

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Goldstein, B. A., Navar, A. M., & Carter, R. E. (2017). Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. Eur Heart J, 38(23), 1805–1814. https://doi.org/10.1093/eurheartj/ehw302
Goldstein, Benjamin A., Ann Marie Navar, and Rickey E. Carter. “Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.Eur Heart J 38, no. 23 (June 14, 2017): 1805–14. https://doi.org/10.1093/eurheartj/ehw302.
Goldstein, Benjamin A., et al. “Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.Eur Heart J, vol. 38, no. 23, June 2017, pp. 1805–14. Pubmed, doi:10.1093/eurheartj/ehw302.
Journal cover image

Published In

Eur Heart J

DOI

EISSN

1522-9645

Publication Date

June 14, 2017

Volume

38

Issue

23

Start / End Page

1805 / 1814

Location

England

Related Subject Headings

  • Risk Factors
  • Risk Assessment
  • Regression Analysis
  • Precision Medicine
  • Models, Biological
  • Machine Learning
  • Humans
  • Cardiovascular System & Hematology
  • Cardiovascular Diseases
  • 3202 Clinical sciences