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

Background: There is growing evidence that social and behavioral determinantsof health (SBDH) play a substantial effect in a wide range of health outcomes.Electronic health records (EHRs) have been widely employed to conductobservational studies in the age of artificial intelligence (AI). However,there has been little research into how to make the most of SBDH informationfrom EHRs. Methods: A systematic search was conducted in six databases to findrelevant peer-reviewed publications that had recently been published. Relevancewas determined by screening and evaluating the articles. Based on selectedrelevant studies, a methodological analysis of AI algorithms leveraging SBDHinformation in EHR data was provided. Results: Our synthesis was driven by ananalysis of SBDH categories, the relationship between SBDH andhealthcare-related statuses, and several NLP approaches for extracting SDOHfrom clinical literature. Discussion: The associations between SBDH and healthoutcomes are complicated and diverse; several pathways may be involved. UsingNatural Language Processing (NLP) technology to support the extraction of SBDHand other clinical ideas simplifies the identification and extraction ofessential concepts from clinical data, efficiently unlocks unstructured data,and aids in the resolution of unstructured data-related issues. Conclusion:Despite known associations between SBDH and disease, SBDH factors are rarelyinvestigated as interventions to improve patient outcomes. Gaining knowledgeabout SBDH and how SBDH data can be collected from EHRs using NLP approachesand predictive models improves the chances of influencing health policy changefor patient wellness, and ultimately promoting health and health equity.  Keywords: Social and Behavioral Determinants of Health, ArtificialIntelligence, Electronic Health Records, Natural Language Processing,Predictive Model

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review