ArticleApplied clinical informatics2020
Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data.
Article in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.
What it found
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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
62 citing papers in PubMed, 2 syntheses or guidelines pooled it, 90 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
- 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
- Exploring approaches to computational representation and classification of user-generated meal logs.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- ClinPreAI: An Agentic AI System for Early Postpartum Depression Risk Prediction from Multimodal EHR Data.medRxiv : the preprint server for health sciences · 2026Article
- Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors.Diagnostics (Basel, Switzerland) · 2026Review
- Using reasoning LLMs to extract SDOH events from clinical notes.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- SDoH-GPT: using large language models to extract social determinants of health.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.Current addiction reports · 2026Review
- Improving social determinants of health documentation in French electronic health records using large language models.Scientific reports · 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
- Modeling Patients' Progression through Health-Related Social Needs.Applied clinical informatics · 2025Article
- Extracting Social Determinants of Health from Dental Clinical Notes.Applied clinical informatics · 2025Article
- Development and validation of computable social phenotypes for health-related social needs.JAMIA open · 2025Article
- Scalable information extraction from free text electronic health records using large language models.BMC medical research methodology · 2025Article
- A Trans and Queer Discursive Approach to Gender Diversity and Misgendering in the Transgender and Gender Diverse Population: Queering a Study for ICD-11.International journal of environmental research and public health · 2025Article
- Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Large-scale identification of social and behavioral determinants of health from clinical notes: comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models.BMC medical informatics and decision making · 2024Article
2 more citing papers are in PubMed but not listed here.
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 at 2 institutions in 1 country.
Funding
Abstract
backgroundSocial and behavioral determinants of health (SBDH) are environmental and behavioral factors that often impede disease management and result in sexually transmitted infections. Despite their importance, SBDH are inconsistently documented in electronic health records (EHRs) and typically collected only in an unstructured format. Evidence suggests that structured data elements present in EHRs can contribute further to identify SBDH in the patient record.
objectiveExplore the automated inference of both the presence of SBDH documentation and individual SBDH risk factors in patient records. Compare the relative ability of clinical notes and structured EHR data, such as laboratory measurements and diagnoses, to support inference.
methodsWe attempt to infer the presence of SBDH documentation in patient records, as well as patient status of 11 SBDH, including alcohol abuse, homelessness, and sexual orientation. We compare classification performance when considering clinical notes only, structured data only, and notes and structured data together. We perform an error analysis across several SBDH risk factors.
resultsClassification models inferring the presence of SBDH documentation achieved good performance (F1 score: 92.7-78.7; F1 considered as the primary evaluation metric). Performance was variable for models inferring patient SBDH risk status; results ranged from F1 = 82.7 for LGBT (lesbian, gay, bisexual, and transgender) status to F1 = 28.5 for intravenous drug use. Error analysis demonstrated that lexical diversity and documentation of historical SBDH status challenge inference of patient SBDH status. Three of five classifiers inferring topic-specific SBDH documentation and 10 of 11 patient SBDH status classifiers achieved highest performance when trained using both clinical notes and structured data.
conclusionOur findings suggest that combining clinical free-text notes and structured data provide the best approach in classifying patient SBDH status. Inferring patient SBDH status is most challenging among SBDH with low prevalence and high lexical diversity.
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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 Socratic graph.