  Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate
action due to their potential devastating consequences on global health.
Point-of-care routine assessments such as electrocardiogram (ECG), can be used
to develop prediction models for identifying individuals at risk. However,
there is often too little clinically-annotated medical data, especially in
early phases of a pandemic, to develop accurate prediction models. In such
situations, historical pre-pandemic health records can be utilized to estimate
a preliminary model, which can then be fine-tuned based on limited available
pandemic data. This study shows this approach -- pre-train deep learning models
with pre-pandemic data -- can work effectively, by demonstrating substantial
performance improvement over three different COVID-19 related diagnostic and
prognostic prediction tasks. Similar transfer learning strategies can be useful
for developing timely artificial intelligence solutions in future pandemic
outbreaks.
