Search Results for author: Collin M. Stultz

Found 5 papers, 3 papers with code

Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals

1 code implementation9 Aug 2023 Hyewon Jeong, Collin M. Stultz, Marzyeh Ghassemi

Additionally, the supervised DML model that uses ECGs with access to 8, 172 mPCWP labels demonstrated significantly better performance on the mPCWP regression task compared to the supervised baseline.

Metric Learning

Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series

no code implementations20 Jul 2023 Aniruddh Raghu, Payal Chandak, Ridwan Alam, John Guttag, Collin M. Stultz

However, most existing SSL methods for clinical time series are limited in that they are designed for unimodal time series, such as a sequence of structured features (e. g., lab values and vitals signs) or an individual high-dimensional physiological signal (e. g., an electrocardiogram).

Self-Supervised Learning Time Series

Data Augmentation for Electrocardiograms

1 code implementation9 Apr 2022 Aniruddh Raghu, Divya Shanmugam, Eugene Pomerantsev, John Guttag, Collin M. Stultz

In experiments, considering three datasets and eight predictive tasks, we find that TaskAug is competitive with or improves on prior work, and the learned policies shed light on what transformations are most effective for different tasks.

Data Augmentation

Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction

1 code implementation4 Mar 2021 Aniruddh Raghu, John Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. Stultz

Inference of latent variables in this model corresponds to both making a prediction and providing supporting evidence for that prediction.

Transferring Knowledge from Text to Predict Disease Onset

no code implementations6 Aug 2016 Yun Liu, Kun-Ta Chuang, Fu-Wen Liang, Huey-Jen Su, Collin M. Stultz, John V. Guttag

Specifically, we use word2vec models trained on a domain-specific corpus to estimate the relevance of each feature's text description to the prediction problem.

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