Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.

ABSTRACT: Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect mach

SUBMITTER: Roland T 

PROVIDER: S-EPMC8960704 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature