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

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dc.contributor.authorSun, W.
dc.contributor.authorKalmady, S.V.
dc.contributor.authorWang, Z.
dc.contributor.authorSalimi, A.
dc.contributor.authorSepehrvand, N.
dc.contributor.authorHindle, Abram
dc.contributor.authorChu, L.M.
dc.contributor.authorGreiner, R.
dc.contributor.authorKaul, P.
dc.date.accessioned2025-05-01T02:06:34Z
dc.date.available2025-05-01T02:06:34Z
dc.date.issued2022
dc.descriptionPandemic 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.
dc.identifier.doihttps://doi.org/10.7939/r3-j19t-y081
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectQ&A communities
dc.subjectGame development
dc.titleImproving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale
dc.typehttp://purl.org/coar/resource_type/R60J-J5BD
ual.jupiterAccesshttp://terms.library.ualberta.ca/public

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