Impact of the Covid-19 pandemic on the performance of machine learning algorithms for predicting perioperative mortality.

ABSTRACT:

Background

Machine-learning models are susceptible to external influences which can result in performance deterioration. The aim of our study was to elucidate the impact of a sudden shift in covariates, like the one caused by the Covid-19 pandemic, on model performance.

Methods

After ethical approval and registration in Clinical Trials (NCT04092933, initial release 17/09/2019), we developed different models for the prediction of perioperative mortality based on preoperative data: one for the pre-pandemic data period until March 2020, one including data before the pandemic and from the first wave until May 2020, and one that covers the complete period before and during the pandemic until October 2021. We applied XGBoost as well as a Deep Learning neural network (DL). Performa

SUBMITTER: Andonov DI 

PROVIDER: S-EPMC10092913 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature