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Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany, University of Bremen, Germany, University of Copenhagen, Denmark
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Epidemiologists can leverage this practical guide, complete with R code, to navigate the complexities of applying machine learning to modern, high-dimensional health data.
Additive explanations of survival models fail because of their inherent non-additivity, but now there's a way to decompose feature interactions into time-dependent components to understand *when* and *why* they fail.
