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

Adherence can be defined as "the extent to which patients take theirmedications as prescribed by their healthcare providers"[Osterberg andBlaschke, 2005]. World Health Organization's reports point out that, indeveloped countries, only about 50% of patients with chronic diseases correctlyfollow their treatments. This severely compromises the efficiency of long-termtherapy and increases the cost of health services. We propose in this paperdifferent models of patient drug consumption in breast cancer treatments. Theaim of these different approaches is to predict medication non-adherence whilegiving insights to doctors of the underlying reasons of these illegitimatedrop-outs. Working with oncologists, we show the interest of Machine- Learningalgorithms fined tune by the feedback of experts to estimate a risk score of apatient's non-adherence and thus improve support throughout their care path.

Machine Learning on Electronic Health Records: Models and Features Usages to predict Medication Non-Adherence