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arXiv · 2211.10431

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

Weijie Sun·Sunil Vasu Kalmady·Nariman Sepehrvand·Luan Manh Chu·Zihan Wang·Amir Salimi·Abram Hindle·Russell Greiner·Padma Kaul
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Abstract

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.

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Weijie Sun, Sunil Vasu Kalmady, Nariman Sepehrvand, Luan Manh Chu, Zihan Wang, Amir Salimi, Abram Hindle, Russell Greiner, Padma Kaul. 2022-11-14. Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale. https://arxiv.org/abs/2211.10431

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