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

• Author(s) / Creator(s)

  • Sun, W.
  • Kalmady, S.V.
  • Wang, Z.
  • Salimi, A.
  • Sepehrvand, N.
  • Hindle, Abram
  • Chu, L.M.
  • Greiner, R.
  • Kaul, P.

• 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

  • Q&A communities
  • Game development

• Type of Item

  Conference/Workshop Presentation

• DOI

  https://doi.org/10.7939/r3-j19t-y081

• License

  Attribution 4.0 International