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

Voting of predictive models for clinical outcomes: consensus of  algorithms for the early prediction of sepsis from clinical data and an  analysis of the PhysioNet/Computing in Cardiology Challenge 2019