  Although there has been significant research in boosting of weak learners,
there has been little work in the field of boosting from strong learners. This
latter paradigm is a form of weighted voting with learned weights. In this
work, we consider the problem of constructing an ensemble algorithm from 70
individual algorithms for the early prediction of sepsis from clinical data. We
find that this ensemble algorithm outperforms separate algorithms, especially
on a hidden test set on which most algorithms failed to generalize.
