ArticleJournal of the American Medical Informatics Association : JAMIA2023
The 2022 n2c2/UW shared task on extracting social determinants of health.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- A data-driven method for research trend analysis in a scientific discipline: Application to the journal of biomedical informatics.Journal of biomedical informatics · 2026Article
- A study of large language models for patient information extraction: Model architecture, fine-tuning strategy, and multi-task instruction tuning.Journal of biomedical informatics · 2026Article
- Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods.JMIR medical informatics · 2026Article
- Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.Patient education and counseling · 2026Article
- Using reasoning LLMs to extract SDOH events from clinical notes.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.Current addiction reports · 2026Review
- Social Work-Supported Strategies to Reduce Postoperative Readmissions in Orthopedic Surgery: A Narrative Review.Cureus · 2026Review
- SDoH-GPT: using large language models to extract social determinants of health.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Improving social determinants of health documentation in French electronic health records using large language models.Scientific reports · 2025Article
- Extracting social determinants of health from electronic health records: development and comparison of rule-based and large language models-based methods.medRxiv : the preprint server for health sciences · 2025Article
- Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- SBDH-Reader: a large language model-powered method for extracting social and behavioral determinants of health from clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Unveiling social determinants of health impact on adverse pregnancy outcomes through natural language processing.Scientific reports · 2025Article
- Tailoring task arithmetic to address bias in models trained on multi-institutional datasets.Journal of biomedical informatics · 2025Article
- Extracting Social Determinants of Health from Dental Clinical Notes.Applied clinical informatics · 2025Article
- Extraction of Social Determinants of Health From Electronic Health Records Using Natural Language Processing.JCO clinical cancer informatics · 2025Article
- Social determinants of health extraction from clinical notes across institutions using large language models.NPJ digital medicine · 2025Article
- Decoding substance use disorder severity from clinical notes using a large language model.Npj mental health research · 2025Article
- Enhancing Cross-Domain Generalizability in Social Determinants of Health Extraction with Prompt-Tuning Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveThe 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 article presents the shared task, data, participating teams, performance results, and considerations for future work. MATERIALS AND
methodsThe 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 3 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).
resultsA 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.
conclusionsSimilar 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, which increase health risks (risk factors) and higher performance achieved for conditions, like substance abstinence and living with family, which reduce health risks (protective factors).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.