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

arXiv: https://arxiv.org/abs/2102.04216 · HTML: https://mlsanity.com/paper/2102.04216/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 117 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 2 of 2)

- [Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction](https://mlsanity.com/paper/2004.05438/) (2020)
- [Recurrent Neural Networks based Obesity Status Prediction Using Activity Data](https://mlsanity.com/paper/1809.07828/) (2018)

## Built on by (most cited first, 8 of 8)

- [The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health](https://mlsanity.com/paper/2301.05571/) (2023)
- [BioMamba: Domain-Adaptive Biomedical Language Models](https://mlsanity.com/paper/2408.02600/) (2024)
- [Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods](https://mlsanity.com/paper/2404.00826/) (2024)
- [Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health](https://mlsanity.com/paper/2306.04059/) (2023)
- [Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text](https://mlsanity.com/paper/2406.06056/) (2024)
- [Academic case reports lack diversity: Assessing the presence and diversity of sociodemographic and behavioral factors related to Post COVID-19 Condition](https://mlsanity.com/paper/2501.12538/) (2025)
- [Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data](https://mlsanity.com/paper/2606.01539/) (2026)
- [Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs](https://mlsanity.com/paper/2606.08483/) (2026)

## Abstract (arXiv)

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