The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges
Open accessAbstract
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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Cited by
Christodoulou, E., Ma, J., Collins, G. S., Steyerberg, E. W., Verbakel, J. Y., & Van Calster, B. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.02.004 · Cited by 2,119
eD octor: machine learning and the future of medicine
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12822 · Cited by 956
Economics of artificial intelligence in healthcare: Diagnosis vs. treatment
Khanna, N. N., Maindarkar, M. A., Viswanathan, V., Fernandes, J. F. E., Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., Kolluri, R., et al. (2022) · Healthcare · doi:10.3390/healthcare10122493 · Cited by 347
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Metadata
- Authors
- Goldstein, B. A., Navar, A. M., & Carter, R. E.
- Venue
- European Heart Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-07-19
- Volume
- 38
- Issue
- 23
- Pages
- ehw302
- ISSN
- 0195-668X (ISSN-L)
- 1522-9645
- OpenAlex
- W2496911238
- Cited by
- 543
- References
- 56
- FWCI
- 47.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Goldstein, B. A., Navar, A. M., & Carter, R. E. (2016). Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. European Heart Journal. https://doi.org/10.1093/eurheartj/ehw302
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
