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We used ensemble machine learning ("Super Learning") to develop a predictor of AKI based on potential risk factors, and evaluated its performance using V-fold cross validation.
In this article, we motivate the application of a data-adaptive estimation approach called super learning (SL) to avoid reliance on arbitrary parametric assumptions in CER.
We applied super learning, an ensembling machine learning method, to develop the PTSD risk score from 67 risk factors that could be assessed within one week of earthquake occurrence.
In addition to developing a risk score that could be implemented in the near future, these results more broadly support the utility of super learning to develop optimized prediction functions for mental health outcomes.
We first developed a prediction model for post-operative AKI based on the approach known as super learning, and then identified the most important risk factors using targeted maximum likelihood estimation (TMLE).
This first step may involve super learning; second, updating the initial regression relying on a fit of the propensity score, in order to obtain an optimal bias-variance trade-off for the parameter of interest.
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That means playing bocce with your friends in the park instead of watching the Super Bowl, learning how to make your own tahini instead of going to Burger King, and never listening to any mainstream radio stations.
"And because we're small, we can iterate super quickly and learn super quickly.
Super Why (PBS): Makes learning reading super fun by incorporating language lessons into classic storylines with a twist.
And last month before the Super Bowlwe learned that online viewership via tablets doubles during big sporting events.
Blackadder, for example, did not coach Carter and McCaw for the best part of eight years in Super Rugby without learning what it takes to get better.
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