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

TL;DR

This paper develops an ensemble voting algorithm from 70 models to improve early sepsis prediction from clinical data, demonstrating superior performance and generalization over individual models.

Contribution

It introduces a novel ensemble method combining multiple algorithms for sepsis prediction, emphasizing boosting from strong learners and demonstrating improved accuracy.

Findings

Ensemble outperforms individual models on test data.

Ensemble generalizes better to unseen data.

Boosting from strong learners enhances prediction accuracy.

Abstract

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.