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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep learning models have shown great promise in various healthcareapplications. However, most models are developed and validated on small-scaledatasets, as collecting high-quality (gold-standard) labels for healthapplications is often costly and time-consuming. As a result, these models maysuffer from overfitting and not generalize well to unseen data. At the sametime, an extensive amount of data with imprecise labels (silver-standard) isstarting to be generally available, as collected from inexpensive wearableslike accelerometers and electrocardiography sensors. These currentlyunderutilized datasets and labels can be leveraged to produce more accurateclinical models. In this work, we propose UDAMA, a novel model with two keycomponents: Unsupervised Domain Adaptation and Multi-discriminator Adversarialtraining, which leverage noisy data from source domain (the silver-standarddataset) to improve gold-standard modeling. We validate our framework on thechallenging task of predicting lab-measured maximal oxygen consumption(VO$_{2}$max), the benchmark metric of cardio-respiratory fitness, usingfree-living wearable sensor data from two cohort studies as inputs. Ourexperiments show that the proposed framework achieves the best performance ofcorr = 0.665 $\pm$ 0.04, paving the way for accurate fitness estimation atscale.

Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction