# The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health

arXiv: https://arxiv.org/abs/2301.05571 · HTML: https://mlsanity.com/paper/2301.05571/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 9 of 9)

- [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)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (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)
- [Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review](https://mlsanity.com/paper/2102.04216/) (2021)
- [Transferability of Neural Network Clinical De-identification Systems](https://mlsanity.com/paper/2102.08517/) (2021)

## Built on by (most cited first, 21 of 21)

- [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023)
- [Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction](https://mlsanity.com/paper/2310.06239/) (2023)
- [Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension](https://mlsanity.com/paper/2303.08262/) (2023)
- [Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning](https://mlsanity.com/paper/2306.07170/) (2023)
- [Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need](https://mlsanity.com/paper/2312.06099/) (2023)
- [Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes](https://mlsanity.com/paper/2310.02451/) (2023)
- [GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models](https://mlsanity.com/paper/2405.20585/) (2024)
- [Leveraging Large Language Models to Extract Information on Substance Use Disorder Severity from Clinical Notes: A Zero-shot Learning Approach](https://mlsanity.com/paper/2403.12297/) (2024)
- [BioMistral-NLU: Towards More Generalizable Medical Language Understanding through Instruction Tuning](https://mlsanity.com/paper/2410.18955/) (2024)
- [Task formulation for Extracting Social Determinants of Health from Clinical Narratives](https://mlsanity.com/paper/2301.11386/) (2023)
- [Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text](https://mlsanity.com/paper/2406.06056/) (2024)
- [PVminer: A Domain-Specific Tool to Detect the Patient Voice in Patient Generated Data](https://mlsanity.com/paper/2602.21165/) (2026)
- [CACER: Clinical Concept Annotations for Cancer Events and Relations](https://mlsanity.com/paper/2409.03905/) (2024)
- [Enhancing Robustness of Foundation Model Representations under Provenance-related Distribution Shifts](https://mlsanity.com/paper/2312.05435/) (2023)
- [A Study of Large Language Models for Patient Information Extraction: Model Architecture, Fine-Tuning Strategy, and Multi-task Instruction Tuning](https://mlsanity.com/paper/2509.04753/) (2025)
- [A Causal Language Modeling Detour Improves Encoder Continued Pretraining](https://mlsanity.com/paper/2605.12438/) (2026)
- [Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning](https://mlsanity.com/paper/2403.12374/) (2024)
- [Improving Social Determinants of Health Documentation in French EHRs Using Large Language Models](https://mlsanity.com/paper/2507.03433/) (2025)
- [NOWJ @BioCreative IX ToxHabits: An Ensemble Deep Learning Approach for Detecting Substance Use and Contextual Information in Clinical Texts](https://mlsanity.com/paper/2602.09469/) (2026)
- [A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP](https://mlsanity.com/paper/2604.06650/) (2026)
- [Using reasoning LLMs to extract SDOH events from clinical notes](https://mlsanity.com/paper/2604.13502/) (2026)

## Abstract (arXiv)

Objective: The n2c2/UW SDOH Challenge explores the extraction of social determinant of health (SDOH) information from clinical notes. The objectives include the advancement of natural language processing (NLP) information extraction techniques for SDOH and clinical information more broadly. This paper presents the shared task, data, participating teams, performance results, and considerations for future work. Materials and Methods: The task used the Social History Annotated Corpus (SHAC), which consists of clinical text with detailed event-based annotations for SDOH events such as alcohol, drug, tobacco, employment, and living situation. Each SDOH event is characterized through attributes related to status, extent, and temporality. The task includes three subtasks related to information extraction (Subtask A), generalizability (Subtask B), and learning transfer (Subtask C). In addressing this task, participants utilized a range of techniques, including rules, knowledge bases, n-grams, word embeddings, and pretrained language models (LM). Results: A total of 15 teams participated, and the top teams utilized pretrained deep learning LM. The top team across all subtasks used a sequence-to-sequence approach achieving 0.901 F1 for Subtask A, 0.774 F1 Subtask B, and 0.889 F1 for Subtask C. Conclusions: Similar to many NLP tasks and domains, pretrained LM yielded the best performance, including generalizability and learning transfer. An error analysis indicates extraction performance varies by SDOH, with lower performance achieved for conditions, like substance use and homelessness, that increase health risks (risk factors) and higher performance achieved for conditions, like substance abstinence and living with family, that reduce health risks (protective factors).