@inproceedings{nademi2023ML4H-supervised-ecg,
 abstract = {An electrocardiogram (ECG) provides crucial information about an individual’s health status. Researchers utilize ECG data to develop learners for a variety of tasks, ranging from diagnosing ECG abnormalities to estimating time to death–here modeled as individual survival distributions (ISDs). The way the ECG is represented is important for creating an effective learner. While many traditional ECG-based prediction models rely on hand-crafted features, such as heart rate, this study aims to achieve a better representation. The effectiveness of various ECG based feature extraction methods for prediction of ISDs, either supervised or unsupervised, have not been explored previously. The study uses a large ECG dataset from 244,077 patients with over 1.6 million 12-lead ECGs, each labeled with the patient {’} s disease {–} one or more International Classification of Diseases (ICD) codes. We explored extracting high-level features from ECG traces using various approaches, then trained models that used these ECG features (along with age and sex), across a range of training sizes, to estimate patient-specific ISDs. The results showed that the supervised feature extractor method produced ECG features that can estimate ISD curves better than ECG features obtained from unsupervised or knowledge-based methods. Supervised ECG features required fewer training instances (as low as 500) to learn ISD models that performed better than the baseline model that only used age and sex. On the other hand, unsupervised and knowledge-based ECG features required over 5,000 training samples to produce ISD models that performed better than the baseline. The study’s findings may assist researchers in selecting the most appropriate approach for extracting high-level features from ECG signals to estimate patient-specific ISD curves.},
 accepted = {2023-11-01},
 author = {Yousef Nademi and Sunil Kalmady and Weijie Sun and Shi-ang Qi and Abram Hindle and Padma Kaul and Russell Greiner},
 authors = {Yousef Nademi and Sunil Kalmady and Weijie Sun and Shi-ang Qi and Abram Hindle and Padma Kaul and Russell Greiner},
 booktitle = {Machine Learning for Health (ML4H) 2023 @ NeurIPS},
 code = {nademi2023ML4H-supervised-ecg},
 date = {2023-12-01},
 doi = {},
 funding = {NSERC Discovery},
 location = {New Orleans, USA},
 pagerange = {368-383},
 pages = {368-383},
 rate = {},
 role = {Co-Author},
 title = {Supervised Electrocardiogram (ECG) Features Outperform Knowledge-based And Unsupervised Features In Individualized Survival Prediction},
 type = {inproceedings},
 url = {http://softwareprocess.ca/pubs/nademi2023ML4H-supervised-ecg.pdf},
 venue = {Machine Learning for Health (ML4H) 2023 @ NeurIPS},
 year = {2023}
}