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

Telemonitoring is the use of electronic devices to monitor patients remotely. A model is needed to translate the data collected by a patient’s mobile device into a predicted score for disease severity assessment. Labeled samples are scarce, which makes it difficult to train a supervised learning model. On the other hand, there is an abundance of samples without precise labels but whose relative rank can be known from domain knowledge. We propose a Ranking-based Weakly Supervised Learning (RWSL) model to integrate both types of data. We apply RWSL to predict Parkinson’s disease severity based on mobile-collected tapping activity data of patients. RWSL achieves high predictive accuracy and outperforms competing methods.

A Ranking-based Weakly Supervised Learning model for telemonitoring of Parkinson’s disease