Learning Models for Diagnosis and Prognosis from Electrocardiogram Data

• Author / Creator

  Sun, Weijie

• The electrocardiogram (ECG) records the electrical activity of a patient’s heart movement. It is one of the standard routine healthcare tests as it is non-invasive and easy to apply. In this thesis, we analyze 2 million ECGs and over 260,000 patients’ health records from the Alberta Health Service, and propose frameworks for learning diagnostic and prognostic models based on supervised learning methods, including ones for survival prediction. First, we learned many models that each use a patient’s ECG to determine if s/he has a specific disease, corresponding to an ICD-10 diagnosis code. Our results show that these diagnosis models can accurately predict numerous health conditions, beyond cardiovascular conditions. Second, we develop ECG diagnosis models for COVID-19 and then use transfer learning to produce models with superior performance. Finally, motivated by the evidence from earlier tasks, we develop binary classification ECG models for predicting all-cause (fixed time) mortality for hospitalized (resp., emergency) patients, and also survival models that produce meaningful survival predictions for each patient. We demonstrate state-of-the-art performance for predicting the time-until-death by using machine learning techniques that first re-express each ECG in latent representations.

• Subjects / Keywords

  • COVID-19
  • ECG
  • Survival
  • Transfer learning
  • Diagnosis
  • Prognosis
  • Deep Learning

• Graduation date

  Spring 2023

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-fwn7-c763

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.