Theresa Roland, Carl Boeck, Thomas Tschoellitsch, Alexander Maletzky, Sepp Hochreiter, Jens Meier, and Guenter Klambauer

Figure 1

Domain shifts in COVID-19 data sets.

We investigate machine learning models that identify COVID-19 positive patients and estimate the mortality risk based on routinely acquired blood tests in a hospital setting. However, during pandemics or new outbreaks, disease and testing characteristics change, thus we face domain shifts. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (taking samples, laboratory), 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. To countermand this effect, we propose methods that first identify domain shifts and then reverse their negative effects on the model performance. Frequent re-training and re-assessment, as well as stronger weighting of more recent samples, keeps model performance and credibility at a high level over time. Our diagnosis models are constructed and tested on large-scale data sets, steadily adapt to observed domain shifts, and maintain high ROC AUC values along pandemics.

medRxiv, 2021-04-09.

View paper
IARAI Authors
Dr Sepp Hochreiter
Health and Well-being
COVID-19, Domain Shift, Machine Learning


Imprint | Privacy Policy

Stay in the know with developments at IARAI

Select list(s)

updates from the Institute.
You can later also tailor your news feed to specific research areas or keywords (Privacy)

Log in with your credentials

Forgot your details?

Create Account