23 citations · 24 across the 5 of their papers we have counts for
6 papers
MS-ECG-FM: Towards a More Universal Electrocardiogram Foundation Model for Health Monitoring using Multi-source Contrastive Learning
Robert A. Lewis, I-Min Chiu, Kyle Verrier +7
Electrocardiography (ECG) records the electrical activity of the heart, aiding diagnosis by detecting abnormalities in cardiac function. ECG foundation models have demonstrated pro…
Anti-causal domain generalization: Leveraging unlabeled data
Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller +3
The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing metho…
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation
Salar Abbaspourazad, Anshuman Mishra, Joseph Futoma +2
Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all b…
Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare
Arno Blaas, Adam Goliński, Andrew Miller +3
We consider robustness to distribution shifts in the context of diagnostic models in healthcare, where the prediction target , e.g., the presence of a disease, is causally upstr…
Large-scale Training of Foundation Models for Wearable Biosignals
Salar Abbaspourazad, Oussama Elachqar, Andrew C. Miller +3
Tracking biosignals is crucial for monitoring wellness and preempting the development of severe medical conditions. Today, wearable devices can conveniently record various biosigna…
Label Shift Estimators for Non-Ignorable Missing Data
Andrew C. Miller, Joseph Futoma
We consider the problem of estimating the mean of a random variable Y subject to non-ignorable missingness, i.e., where the missingness mechanism depends on Y . We connect the auxi…