Weak Supervision for Affordable Modeling of Electrocardiogram Data
By Mononito Goswami, Benedikt Boecking, Artur Dubrawski. 2022
16 citations recorded by the source corpus.
Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose heart disease. ECG studies using Machine Learning to automatically detect abnormal heartbeats so far depend on large, manually annotated datasets. While collecting vast amounts of unlabeled data can be straightforward, the point-by-point annotation of abnormal heartbeats is tedious and expensive. We explore the use of multiple weak supervision sources to learn diagnostic models of abnormal heartbeats via human designed heuristics, without using ground truth labels on individual data points. Our work is among the first to define weak supervision sources directly on time series data. Results show that with as few as six intuitive time series heuristics, we are able to infer high quality probabilistic label estimates for over 100,000 heartbeats with little human effort, and use the estimated labels to train competitive classifiers evaluated on held out test data.
Subfields: Artificial Intelligence, Cardiology and Cardiovascular Medicine, Medical & Health AI, Signal Processing
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Citations in the corpus
- Are We Done with MMLU? (2024, neutral): “…[28, 29, 30]; automated labelling processes such as crowdsourcing [31] — where annotators may be optimizing for their own incentives; human errors [32] — e.g., lack of expertise on a given topic; or programmatic weak supervision [33, 34, 7], and personal [35] or group-level [36] annotator biases.”
- MOMENT: A Family of Open Time-series Foundation Models (2024, background): “Time series analysis is an important field encompassing a wide range of applications ranging from forecasting weather patterns (Schneider & Dickinson, 1974) or detecting irregular heartbeats using Electrocardiograms (Goswami et al., 2021), to identifying anomalous software deployments (Xu et al., 20”