ArticleJAMIA open2021
Identification of social determinants of health using multi-label classification of electronic health record clinical notes.
Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses 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
43 citing papers in PubMed, 3 syntheses or guidelines 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
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Extracting social determinants of health from electronic health records using natural language processing: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Scalable extraction of social determinants of health from clinical notes in a sepsis cohort using instruction-tuned language models.JAMIA open · 2026Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical 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
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 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
- Predicting Behavioral Determinants of Health from Clinical Text Using Transformer Models and BiLSTM.medRxiv : the preprint server for health sciences · 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
- Testing regular expression searches and machine learning models to determine housing instability and low income status from primary care electronic medical record data in Toronto, Ontario.BMC public health · 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
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Protocol for a Systematic Review.JMIR research protocols · 2025Article
- On the development and validation of large language model-based classifiers for identifying social determinants of health.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Social Phenotyping for Cardiovascular Risk Stratification in Electronic Health Registries.Current atherosclerosis reports · 2024Review
- Precision Prevention: Using Data to Target the Right Intervention at the Right Intensity in the Right Community at the Right Time.Yearbook of medical informatics · 2024Article
- Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.Journal of biomedical informatics · 2024Article
- Structured and unstructured social risk factor documentation in the electronic health record underestimates patients' self-reported risks.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectivesSocial determinants of health (SDH), key contributors to health, are rarely systematically measured and collected in the electronic health record (EHR). We investigate how to leverage clinical notes using novel applications of multi-label learning (MLL) to classify SDH in mental health and substance use disorder patients who frequent the emergency department. METHODS AND MATERIALS: We labeled a gold-standard corpus of EHR clinical note sentences (
resultsOur results suggested that, overall, BI-LSTM outperformed the other classification models in terms of AUC-ROC (93.9), AP (0.76), and Hamming loss (0.12). The AUC-ROC values of MLL models of SDH related domains varied between (0.59-1.0). We found that 44.6% of our study population ( DISCUSSION AND
conclusionThe proposed approach of training an MLL model on an SDH rich data source can produce a high performing classifier using only unstructured clinical notes. We also provide evidence that model performance is associated with lexical diversity by health professionals and the auto-generation of clinical note sentences to document SDH.
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.