52 citations · 68 across the 6 of their papers we have counts for
6 papers
Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals
Hyewon Jeong, Collin M. Stultz, Marzyeh Ghassemi
Heart failure is a debilitating condition that affects millions of people worldwide and has a significant impact on their quality of life and mortality rates. An objective assessme…
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series
Aniruddh Raghu, Payal Chandak, Ridwan Alam +2
Self-supervised learning (SSL) for clinical time series data has received significant attention in recent literature, since these data are highly rich and provide important informa…
Data Augmentation for Electrocardiograms
Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev +2
Neural network models have demonstrated impressive performance in predicting pathologies and outcomes from the 12-lead electrocardiogram (ECG). However, these models often need to…
Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling
Nathaniel Diamant, Erik Reinertsen, Steven Song +3
Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate this effect of small sample size, we introduce a p…
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction
Aniruddh Raghu, John Guttag, Katherine Young +3
The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, w…
Transferring Knowledge from Text to Predict Disease Onset
Yun Liu, Kun-Ta Chuang, Fu-Wen Liang +3
In many domains such as medicine, training data is in short supply. In such cases, external knowledge is often helpful in building predictive models. We propose a novel method to i…