ArticleJournal of the American Medical Informatics Association : JAMIA2023
Automatic extraction of social determinants of health from medical notes of chronic lower back pain patients.
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 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Article
- Article
- Extracting Social Determinants of Health From Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model Methods.JMIR medical informatics · 2026Article
- Life events extraction from healthcare notes for veteran acute suicide risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- SDoH-GPT: using large language models to extract social determinants of health.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine Learning.medRxiv : the preprint server for health sciences · 2025Article
- Examining housing insecurity and transportation barriers in pediatric hospital readmissions: insights from structured and unstructured data.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Clinical Information Extraction From Notes of Veterans With Lymphoid Malignancies: Natural Language Processing Study.JMIR medical informatics · 2025Article
- Unveiling social determinants of health impact on adverse pregnancy outcomes through natural language processing.Scientific reports · 2025Article
- Social determinants of health extraction from clinical notes across institutions using large language models.NPJ digital medicine · 2025Article
- Clinical Significance of Marital Status and Changes in Status Extracted from Unstructured Clinical Notes Using Ensembles of Off-the-Shelf Extraction Models.Research square · 2025Article
- Extracting Housing and Food Insecurity Information From Clinical Notes Using cTAKES.Health services research · 2025Article
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Protocol for a Systematic Review.JMIR research protocols · 2025Article
- Natural Language Processing and Social Determinants of Health in Mental Health Research: AI-Assisted Scoping Review.JMIR mental health · 2025Article
- Article
- Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.Frontiers in neurology · 2025Article
- Evaluating associations between social risks and health care utilization in patients with chronic low back pain.Pain reports · 2024Article
- The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain.Current pain and headache reports · 2024Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe applied natural language processing and inference methods to extract social determinants of health (SDoH) information from clinical notes of patients with chronic low back pain (cLBP) to enhance future analyses of the associations between SDoH disparities and cLBP outcomes. MATERIALS AND
methodsClinical notes for patients with cLBP were annotated for 7 SDoH domains, as well as depression, anxiety, and pain scores, resulting in 626 notes with at least one annotated entity for 364 patients. We used a 2-tier taxonomy with these 10 first-level classes (domains) and 52 second-level classes. We developed and validated named entity recognition (NER) systems based on both rule-based and machine learning approaches and validated an entailment model.
resultsAnnotators achieved a high interrater agreement (Cohen's kappa of 95.3% at document level). A rule-based system (cTAKES), RoBERTa NER, and a hybrid model (combining rules and logistic regression) achieved performance of F1 = 47.1%, 84.4%, and 80.3%, respectively, for first-level classes. DISCUSSION: While the hybrid model had a lower F1 performance, it matched or outperformed RoBERTa NER model in terms of recall and had lower computational requirements. Applying an untuned RoBERTa entailment model, we detected many challenging wordings missed by NER systems. Still, the entailment model may be sensitive to hypothesis wording.
conclusionThis study developed a corpus of annotated clinical notes covering a broad spectrum of SDoH classes. This corpus provides a basis for training machine learning models and serves as a benchmark for predictive models for NER for SDoH and knowledge extraction from clinical texts.
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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.