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

More than one million people commit suicide every year worldwide. The costsof daily cares, social stigma and treatment issues are still hard barriers toovercome in mental health. Most symptoms of mental disorders are related to thebehavioral state of a patient, such as the mobility or social activity.Mobile-based technologies allow the passive collection of patients data, whichsupplements conventional assessments that rely on biased questionnaires andoccasional medical appointments. In this work, we present a non-invasivemachine learning (ML) model to detect behavioral shifts in psychiatric patientsfrom unobtrusive data collected by a smartphone app. Our clinically validatedresults shed light on the idea of an early detection mobile tool for the taskof suicide attempt prevention.

Passive detection of behavioral shifts for suicide attempt prevention