ArticleJournal of the American Medical Informatics Association : JAMIA2023
Extracting social determinants of health from clinical note text with classification and sequence-to-sequence approaches.
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 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 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
- Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods.JMIR medical informatics · 2026Article
- Using reasoning LLMs to extract SDOH events from clinical notes.Proceedings. IEEE International Conference on Healthcare Informatics · 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
- Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records.NPJ digital medicine · 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
- Article
- CACER: Clinical concept Annotations for Cancer Events and Relations.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer-Based Investigation.JMIR medical informatics · 2024Article
- Large language models to identify social determinants of health in electronic health records.NPJ digital medicine · 2024Article
- Boosting Social Determinants of Health Extraction with Semantic Knowledge Augmented Large Language Model.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Zero-shot Learning with Minimum Instruction to Extract Social Determinants and Family History from Clinical Notes using GPT Model.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2023Article
- Advancements in extracting social determinants of health information from narrative text.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Balancing Model Complexity and Clinical Deployability in Deep Learning for Sociodemographic Information Extraction.Journal of primary care & community healthArticle
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveSocial determinants of health (SDOH) are nonmedical factors that can influence health outcomes. This paper seeks to extract SDOH from clinical texts in the context of the National NLP Clinical Challenges (n2c2) 2022 Track 2 Task. MATERIALS AND
methodsAnnotated and unannotated data from the Medical Information Mart for Intensive Care III (MIMIC-III) corpus, the Social History Annotation Corpus, and an in-house corpus were used to develop 2 deep learning models that used classification and sequence-to-sequence (seq2seq) approaches.
resultsThe seq2seq approach had the highest overall F1 scores in the challenge's 3 subtasks: 0.901 on the extraction subtask, 0.774 on the generalizability subtask, and 0.889 on the learning transfer subtask. DISCUSSION: Both approaches rely on SDOH event representations that were designed to be compatible with transformer-based pretrained models, with the seq2seq representation supporting an arbitrary number of overlapping and sentence-spanning events. Models with adequate performance could be produced quickly, and the remaining mismatch between representation and task requirements was then addressed in postprocessing. The classification approach used rules to generate entity relationships from its sequence of token labels, while the seq2seq approach used constrained decoding and a constraint solver to recover entity text spans from its sequence of potentially ambiguous tokens.
conclusionWe proposed 2 different approaches to extract SDOH from clinical texts with high accuracy. However, accuracy suffers on text from new healthcare institutions not present in the training data, and thus generalization remains an important topic for future study.
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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.