Susceptibility of AutoML mortality prediction algorithms to model drift caused by the COVID pandemic.

ABSTRACT:

Background

Concept drift and covariate shift lead to a degradation of machine learning (ML) models. The objective of our study was to characterize sudden data drift as caused by the COVID pandemic. Furthermore, we investigated the suitability of certain methods in model training to prevent model degradation caused by data drift.

Methods

We trained different ML models with the H2O AutoML method on a dataset comprising 102,666 cases of surgical patients collected in the years 2014-2019 to predict postoperative mortality using preoperatively available data. Models applied were Generalized Linear Model with regularization, Default Random Forest, Gradient Boosting Machine, eXtreme Gradient Boosting, Deep Learning and Stacked Ensembles comprising all base models. Further, we modif

SUBMITTER: Kagerbauer SM 

PROVIDER: S-EPMC10837894 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature