论文标题
对临床结果的预测模型的投票：从临床数据早期预测败血症的算法的共识，以及对心脏病学挑战的生理学/计算的分析2019
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
论文作者
论文摘要
尽管在促进弱学习者方面进行了重大研究，但在强大的学习者的促进领域中，工作很少。后一种范式是一种具有学识渊博的权重的加权投票形式。在这项工作中，我们考虑了从70种单独算法中构建一个集成算法的问题，以早日从临床数据中预测败血症。我们发现，该集成算法的表现优于单独的算法，尤其是在大多数算法都无法概括的隐藏测试集上。
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.
