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
This work addresses the problem of early sepsis prediction for clinicians, providing an incremental improvement in predictive accuracy.
The authors developed an ensemble algorithm by combining 70 individual algorithms to predict sepsis from clinical data. This ensemble approach demonstrated superior performance compared to individual algorithms, particularly on a hidden test set where many individual algorithms struggled to generalize.
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.