@inproceedings{sun2022TS4H-improving-ecg-covid,
 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.},
 author = {Weijie Sun and Sunil Vasu Kalmady and Zihan Wang and Amir Salimi and Nariman Sepehrvand and Abram Hindle and Luan Manh Chu and Russell Greiner and Padma Kaul},
 authors = {Weijie Sun and Sunil Vasu Kalmady and Zihan Wang and Amir Salimi and Nariman Sepehrvand and Abram Hindle and Luan Manh Chu and Russell Greiner and Padma Kaul},
 booktitle = {NeurIPS TS4H: Timeseries for Health},
 code = {sun2022TS4H-improving-ecg-covid},
 funding = {NSERC Discovery},
 location = {New Orleans, United States},
 pages = {1--9},
 rate = {Unknown},
 role = {Co-Author},
 title = {Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale},
 type = {inproceedings},
 url = {http://softwareprocess.ca/pubs/sun2022TS4H-improving-ecg-covid.pdf},
 venue = {NeurIPS TS4H: Timeseries for Health},
 year = {2022}
}