ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 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
- Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review.JMIR medical informatics · 2025Pooled it
- Demonstrating a Social Intelligence Analysis Framework for Loneliness: Infodemiology Approach.Online journal of public health informatics · 2026Article
- SDoH-GPT: using large language models to extract social determinants of health.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Leveraging large language models to extract smoking history from clinical notes for lung cancer surveillance.NPJ digital medicine · 2025Article
- 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
- Social determinants of health extraction from clinical notes across institutions using large language models.NPJ digital medicine · 2025Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review.JMIR cancer · 2025Article
- Large language models in cancer: potentials, risks, and safeguards.BJR artificial intelligence · 2025Review
- Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence.Circulation. Arrhythmia and electrophysiology · 2025Article
- Article
- 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
- A fair individualized polysocial risk score for identifying increased social risk in type 2 diabetes.Nature communications · 2024Article
- Classifying Individuals With Rheumatic Conditions as Financially Insecure Using Electronic Health Record Data and Natural Language Processing: Algorithm Derivation and Validation.ACR open rheumatology · 2024Article
- Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.Journal of biomedical informatics · 2024Article
- Large language models to identify social determinants of health in electronic health records.NPJ digital medicine · 2024Article
- Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions.Journal of clinical and translational science · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Social and behavioral determinants of health (SBDoH) have important roles in shaping people's health. In clinical research studies, especially comparative effectiveness studies, failure to adjust for SBDoH factors will potentially cause confounding issues and misclassification errors in either statistical analyses and machine learning-based models. However, there are limited studies to examine SBDoH factors in clinical outcomes due to the lack of structured SBDoH information in current electronic health record (EHR) systems, while much of the SBDoH information is documented in clinical narratives. Natural language processing (NLP) is thus the key technology to extract such information from unstructured clinical text. However, there is not a mature clinical NLP system focusing on SBDoH. In this study, we examined two state-of-the-art transformer-based NLP models, including BERT and RoBERTa, to extract SBDoH concepts from clinical narratives, applied the best performing model to extract SBDoH concepts on a lung cancer screening patient cohort, and examined the difference of SBDoH information between NLP extracted results and structured EHRs (SBDoH information captured in standard vocabularies such as the International Classification of Diseases codes). The experimental results show that the BERT-based NLP model achieved the best strict/lenient F1-score of 0.8791 and 0.8999, respectively. The comparison between NLP extracted SBDoH information and structured EHRs in the lung cancer patient cohort of 864 patients with 161,933 various types of clinical notes showed that much more detailed information about smoking, education, and employment were only captured in clinical narratives and that it is necessary to use both clinical narratives and structured EHRs to construct a more complete picture of patients' SBDoH factors.
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35309014PMC8861705What 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.