  Background: There is growing evidence that social and behavioral determinants
of health (SBDH) play a substantial effect in a wide range of health outcomes.
Electronic health records (EHRs) have been widely employed to conduct
observational studies in the age of artificial intelligence (AI). However,
there has been little research into how to make the most of SBDH information
from EHRs. Methods: A systematic search was conducted in six databases to find
relevant peer-reviewed publications that had recently been published. Relevance
was determined by screening and evaluating the articles. Based on selected
relevant studies, a methodological analysis of AI algorithms leveraging SBDH
information in EHR data was provided. Results: Our synthesis was driven by an
analysis of SBDH categories, the relationship between SBDH and
healthcare-related statuses, and several NLP approaches for extracting SDOH
from clinical literature. Discussion: The associations between SBDH and health
outcomes are complicated and diverse; several pathways may be involved. Using
Natural Language Processing (NLP) technology to support the extraction of SBDH
and other clinical ideas simplifies the identification and extraction of
essential 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 rarely
investigated as interventions to improve patient outcomes. Gaining knowledge
about SBDH and how SBDH data can be collected from EHRs using NLP approaches
and predictive models improves the chances of influencing health policy change
for patient wellness, and ultimately promoting health and health equity.
  Keywords: Social and Behavioral Determinants of Health, Artificial
Intelligence, Electronic Health Records, Natural Language Processing,
Predictive Model
