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ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasets
Weijie Sun · Sunil Vasu Kalmady · Amir Salimi · Russell Greiner · Padma Kaul

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in Medical Imaging meets NeurIPS »

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 selected patient cohorts have been limited to a small set of cardiac diseases. In this study, we use a population-based dataset of >250,000 patients with >1000 medical conditions and >2 million ECGs to identify a wide range of diseases that could be accurately diagnosed from the patient’s first in-hospital ECG. Our DL models uncovered 128 diseases and 68 disease categories with strong discriminative performance.

Author Information

Weijie Sun (Canadian VIGOUR Centre, University of Alberta)
Sunil Vasu Kalmady (University of Alberta)
Sunil Vasu Kalmady

Adjunct Professor, Faculty of Science - Computing Science, University of Alberta. Senior Machine Learning Specialist, Faculty of Medicine & Dentistry - Medicine Dept, University of Alberta. My professional goal is to advance the options for personalized treatment of complex medical disorders via application of machine learning and data science. To this end, I have undergone extensive post-doctoral training in Alberta Machine Intelligence Institute (AMII), one of Canada’s three artificial intelligence centers of excellence. My qualifications are complemented by over 5 years of research experience in developing, evaluating and deploying machine learning models using various structured and unstructured real-word healthcare datasets. In my current position as an adjunct professor of computing science and a senior machine learning specialist, I focus on developing learning tools to predict prognostic outcomes in cardiovascular diseases using electronic medical records, electrocardiograms and echocardiograms at the population scale. In the past, I have developed successful AI methods to identify and predict specific symptom clusters and treatment responses in psychiatric disorders such as Schizophrenia and OCD using multimodal imaging, which have been published in distinguished journals such as Nature Schizophrenia and featured in several news reports.

Amir Salimi (University of Alberta)
Russell Greiner (University of Alberta)
Padma Kaul (University of Alberta)

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