English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Supervised machine learning applications in health care are often limited dueto a scarcity of labeled training data. To mitigate this effect of small samplesize, we introduce a pre-training approach, Patient Contrastive Learning ofRepresentations (PCLR), which creates latent representations of ECGs from alarge number of unlabeled examples. The resulting representations areexpressive, performant, and practical across a wide spectrum of clinical tasks.We develop PCLR using a large health care system with over 3.2 million 12-leadECGs, and demonstrate substantial improvements across multiple new tasks whenthere are fewer than 5,000 labels. We release our model to extract ECGrepresentations athttps://github.com/broadinstitute/ml4h/tree/master/model_zoo/PCLR.

Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling