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

Electronic medical records (EMRs) supports the development of machinelearning algorithms for predicting disease incidence, patient response totreatment, and other healthcare events. But insofar most algorithms have beencentralized, taking little account of the decentralized, non-identicallyindependently distributed (non-IID), and privacy-sensitive characteristics ofEMRs that can complicate data collection, sharing and learning. To address thischallenge, we introduced a community-based federated machine learning (CBFL)algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered thedistributed data into clinically meaningful communities that captured similardiagnoses and geological locations, and learnt one model for each community.Throughout the learning process, the data was kept local on hospitals, whilelocally-computed results were aggregated on a server. Evaluation results showthat CBFL outperformed the baseline FL algorithm in terms of Area Under theReceiver Operating Characteristic Curve (ROC AUC), Area Under thePrecision-Recall Curve (PR AUC), and communication cost between hospitals andthe server. Furthermore, communities' performance difference could be explainedby how dissimilar one community was to others.

Patient Clustering Improves Efficiency of Federated Machine Learning to  predict mortality and hospital stay time using distributed Electronic Medical  Records