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Skip to Search Results- 2Electrocardiogram (ECG)
- 1Cardiac abnormalities
- 1Cardiovascular diseases
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An Empirical Study to Investigate Collaboration Among Developers in Open Source Software (OSS)
Download2023
Sun, W., Iwuchukwu, S., Bangash, A.A., Hindle, Abram
The value of teamwork is being recognized by project owners, resulting in an increased acknowledgement of collaboration among developers in software engineering. A good understanding of how developers work together could positively impact software development practices. In this paper, we...
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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
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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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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...
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Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms
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Sun, W., Kalmady, S.V., Sepehrvand, N., Salimi, A., Nademi, Y., Bainey, K., Ezekowitz, J.A., Greiner, R., Hindle, Abram, McAlister, F.A., Sandhu, R.K., Kaul, P.
The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality...