ArticleJournal of pain and symptom management2024
Symptom-BERT: Enhancing Cancer Symptom Detection in EHR Clinical Notes.
Article in Journal of pain and symptom management, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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Who cites it
7 citing papers in PubMed.
- Development of a benchmarking dataset for symptom detection using large language models.JAMIA open · 2026Article
- A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives.Journal of healthcare informatics research · 2026Review
- Leveraging Natural Language Processing for Symptom Identification in Acute Myeloid Leukemia Using Clinical Notes from Electronic Health Records.Cancer nursing · 2026Article
- An Informatics Approach to Characterizing Rarely Documented Clinical Information in Electronic Health Records: Spiritual Care as an Exemplar.Applied clinical informatics · 2025Article
- A novel dual embedding few-shot learning approach for classifying bone loss using orthopantomogram radiographic notes.Head & face medicine · 2025Article
- Large language models in cancer: potentials, risks, and safeguards.BJR artificial intelligence · 2025Review
- Predictors of Concordance between Patient-Reported and Provider-Documented Symptoms in the Context of Cancer and Multimorbidity.Applied clinical informatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
contextExtracting cancer symptom documentation allows clinicians to develop highly individualized symptom prediction algorithms to deliver symptom management care. Leveraging advanced language models to detect symptom data in clinical narratives can significantly enhance this process.
objectiveThis study uses a pretrained large language model to detect and extract cancer symptoms in clinical notes.
methodsWe developed a pretrained language model to identify cancer symptoms in clinical notes based on a clinical corpus from the Enterprise Data Warehouse for Research at a healthcare system in the Midwestern United States. This study was conducted in 4 phases:
resultsThe Symptom-BERT model effectively detected cancer symptoms in clinical notes. It achieved results with a micro-averaged F1-score of 0.933, an AUC of 0.929 internally, and 0.831 and 0.834 externally. Our analysis shows that physical symptoms, like Pruritus, are typically identified with higher performance than psychological symptoms, such as anxiety.
conclusionThis study underscores the transformative potential of specialized pretraining on domain-specific data in boosting the performance of language models for medical applications. The Symptom-BERT model's exceptional efficacy in detecting cancer symptoms heralds a groundbreaking stride in patient-centered AI technologies, offering a promising path to elevate symptom management and cultivate superior patient self-care outcomes.
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What Socratic 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 Socratic graph.