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- Title
Refining and simplifying decision models—tackling the 'one size fits all' challenge.
- Authors
Lamata, Pablo
- Abstract
These findings highlight that variables initially found to be redundant can still add robustness and better generality of prediction performance, especially if those additional variables do not originate from the same experimental test (i.e. the same SPECT image). This editorial refers to 'Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry' by R. Rios I et al. i , https://doi.org/10.1093/cvr/cvab236. An important methodological consideration is the potential gain of advanced machine learning (ML) choices instead of conventional options when building the risk prediction model.
- Subjects
COMPUTER-assisted image analysis (Medicine); SINGLE-photon emission computed tomography; HEART valve prosthesis implantation
- Publication
Cardiovascular Research, 2022, Vol 118, Issue 9, p2037
- ISSN
0008-6363
- Publication type
Article
- DOI
10.1093/cvr/cvac083