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- 2PhysioNet/CinC
- 1Cardiac abnormalities
- 1Cardiovascular diseases
- 1DL model
- 1Deep learning (DL)
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ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasets
Download2021
Sun, W., Kalmady, S.V., Salimi, A.S., Sepehrvand, N., Ly, E., Hindle, Abram, Greiner, R., Kaul, P.
Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in...
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Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale
Download2022
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....
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2021
Wong, A.W., Salimi, A., Hindle, Abram, Kalmady, S.V., Kaul, P.
The 12-lead electrocardiogram (ECG) measures the electrical activity of the heart for physicians to use in diagnosing cardiac disorders. This paper investigates the multi-label, multi-class classification of ECG records into one or more of 27 possible medical diagnoses. Our multi-step approach...
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2020
Wong, A.W., Sun, W., Kalmady, S.V., Kaul, P., Hindle, Abram
The 12-lead electrocardiogram (ECG) is a commonly used tool for detecting cardiac abnormalities such as atrial fibrillation, blocks, and irregular complexes. For the PhysioNet/CinC 2020 Challenge, we built an algorithm using gradient boosted tree ensembles fitted on morphology and signal...