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

By Benjamin Alan Goldstein, Ann Marie Navar, Rickey E. Carter. European Heart Journal, 2016

484 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/a5c0309b9895066ebd08acfe326b01ce2fdefdd4

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In Artificial Intelligence

In Cardiology and Cardiovascular Medicine

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Citations in the corpus

eDoctor: machine learning and the future of medicine (2018, background): “…– predicting continuous values for a target variable based on training set instances with known values of that variable such as when calculating risk of cardiovascular disease, predicting tumour size, estimating individualized disease-free survival or predicting length of hospital stay [17].”
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “Some researchers claim that ML will not outperform LR when only a limited set of prespeciﬁed predictors is considered, and that the advantage of ML lies in better handling a huge amount of predictors [3,7,12,15,16,104].”