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Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale

  • Author(s) / Creator(s)
  • Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-ofcare 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.

  • Date created
    2022
  • Subjects / Keywords
  • Type of Item
    Conference/Workshop Presentation
  • DOI
    https://doi.org/10.7939/r3-j19t-y081
  • License
    Attribution 4.0 International