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

Language models have the potential to assess mental health using social mediadata. By analyzing online posts and conversations, these models can detectpatterns indicating mental health conditions like depression, anxiety, orsuicidal thoughts. They examine keywords, language markers, and sentiment togain insights into an individual's mental well-being. This information iscrucial for early detection, intervention, and support, improving mental healthcare and prevention strategies. However, using language models for mentalhealth assessments from social media has two limitations: (1) They do notcompare posts against clinicians' diagnostic processes, and (2) It'schallenging to explain language model outputs using concepts that the cliniciancan understand, i.e., clinician-friendly explanations. In this study, weintroduce Process Knowledge-infused Learning (PK-iL), a new learning paradigmthat layers clinical process knowledge structures on language model outputs,enabling clinician-friendly explanations of the underlying language modelpredictions. We rigorously test our methods on existing benchmark datasets,augmented with such clinical process knowledge, and release a new dataset forassessing suicidality. PK-iL performs competitively, achieving a 70% agreementwith users, while other XAI methods only achieve 47% agreement (averageinter-rater agreement of 0.72). Our evaluations demonstrate that PK-iLeffectively explains model predictions to clinicians.

Process Knowledge-infused Learning for Clinician-friendly Explanations