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

2020/12/20 by Matthew A. Reyna, Gari D. Clifford, Reyna, Matthew A. +1
Computer Science · Medicine · #Clinical Reasoning and Diagnostic Skills #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Sepsis Diagnosis and Treatment

paper · pdf · doi:10.48550/arxiv.2012.11013

openalex publication_date 2020/12/20 · openalex created_date 2021/01/05 · openalex updated_date 2026/07/28

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.

Citations

• Simple Fast Algorithms for the Editing Distance between Trees and Related Problems
• Groups of diverse problem solvers can outperform groups of high-ability problem solvers
• PhysioBank, PhysioToolkit, and PhysioNet
• An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU
• Generalizability of predictive models for intensive care unit patients
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019
• The Future AI in Healthcare: A Tsunami of False Alarms or a Product of\n Experts?

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