# A Marker-based Neural Network System for Extracting Social Determinants of Health

arXiv: https://arxiv.org/abs/2212.12800 · HTML: https://mlsanity.com/paper/2212.12800/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 24 Dec 2022.

## Builds on (most cited first, 15 of 15)

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- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
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- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [Neural Architectures for Nested NER through Linearization](https://mlsanity.com/paper/1908.06926/) (2019)
- [Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER](https://mlsanity.com/paper/2110.08454/) (2021)
- [Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction](https://mlsanity.com/paper/2004.05438/) (2020)
- [KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation](https://mlsanity.com/paper/2101.00419/) (2021)
- [Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition](https://mlsanity.com/paper/2110.07480/) (2021)
- [Combining Spans into Entities: A Neural Two-Stage Approach for Recognizing Discontiguous Entities](https://mlsanity.com/paper/1909.00930/) (2019)

## Abstract (arXiv)

Objective. The impact of social determinants of health (SDoH) on patients' healthcare quality and the disparity is well-known. Many SDoH items are not coded in structured forms in electronic health records. These items are often captured in free-text clinical notes, but there are limited methods for automatically extracting them. We explore a multi-stage pipeline involving named entity recognition (NER), relation classification (RC), and text classification methods to extract SDoH information from clinical notes automatically. Materials and Methods. The study uses the N2C2 Shared Task data, which was collected from two sources of clinical notes: MIMIC-III and University of Washington Harborview Medical Centers. It contains 4480 social history sections with full annotation for twelve SDoHs. In order to handle the issue of overlapping entities, we developed a novel marker-based NER model. We used it in a multi-stage pipeline to extract SDoH information from clinical notes. Results. Our marker-based system outperformed the state-of-the-art span-based models at handling overlapping entities based on the overall Micro-F1 score performance. It also achieved state-of-the-art performance compared to the shared task methods. Conclusion. The major finding of this study is that the multi-stage pipeline effectively extracts SDoH information from clinical notes. This approach can potentially improve the understanding and tracking of SDoHs in clinical settings. However, error propagation may be an issue, and further research is needed to improve the extraction of entities with complex semantic meanings and low-resource entities using external knowledge.