# Leveraging Natural Language Processing to Augment Structured Social Determinants of Health Data in the Electronic Health Record

arXiv: https://arxiv.org/abs/2212.07538 · HTML: https://mlsanity.com/paper/2212.07538/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction](https://mlsanity.com/paper/2004.05438/) (2020)
- [A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models](https://mlsanity.com/paper/2108.04949/) (2021)

## Built on by (most cited first, 7 of 7)

- [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023)
- [Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning](https://mlsanity.com/paper/2306.07170/) (2023)
- [Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text](https://mlsanity.com/paper/2406.06056/) (2024)
- [Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes](https://mlsanity.com/paper/2305.08777/) (2023)
- [CACER: Clinical Concept Annotations for Cancer Events and Relations](https://mlsanity.com/paper/2409.03905/) (2024)
- [Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts](https://mlsanity.com/paper/2306.09544/) (2023)
- [Using reasoning LLMs to extract SDOH events from clinical notes](https://mlsanity.com/paper/2604.13502/) (2026)

## Abstract (arXiv)

Objective: Social determinants of health (SDOH) impact health outcomes and are documented in the electronic health record (EHR) through structured data and unstructured clinical notes. However, clinical notes often contain more comprehensive SDOH information, detailing aspects such as status, severity, and temporality. This work has two primary objectives: i) develop a natural language processing (NLP) information extraction model to capture detailed SDOH information and ii) evaluate the information gain achieved by applying the SDOH extractor to clinical narratives and combining the extracted representations with existing structured data. Materials and Methods: We developed a novel SDOH extractor using a deep learning entity and relation extraction architecture to characterize SDOH across various dimensions. In an EHR case study, we applied the SDOH extractor to a large clinical data set with 225,089 patients and 430,406 notes with social history sections and compared the extracted SDOH information with existing structured data. Results: The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found extracted SDOH information complements existing structured data with 32% of homeless patients, 19% of current tobacco users, and 10% of drug users only having these health risk factors documented in the clinical narrative. Conclusions: Utilizing EHR data to identify SDOH health risk factors and social needs may improve patient care and outcomes. Semantic representations of text-encoded SDOH information can augment existing structured data, and this more comprehensive SDOH representation can assist health systems in identifying and addressing these social needs.