ArticleJCO clinical cancer informatics2024
Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.
Article in JCO clinical cancer informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07038434 (Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations), which is not on this map. Cited by 8 papers.
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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.
Refining mUltiple Artificial intelliGence strateGies for Automatic Pain Assessment Investigations: RUGGI Study
Who cites it
8 citing papers in PubMed.
- Drivers of Artificial Intelligence (AI) Adoption in Supporting Chronic Disease Management in Primary Care: A Scoping Review.Nursing reports (Pavia, Italy) · 2026Review
- Research Study Attrition and Participant-Informed Retention Strategies Among Cancer Survivors.JAMA network open · 2026Article
- Refining multiple artificial intelligence strategies for automatic pain assessment investigations (RUGGI Study): A study protocol.European journal of anaesthesiology and intensive care · 2026Article
- Leveraging Natural Language Processing for Symptom Identification in Acute Myeloid Leukemia Using Clinical Notes from Electronic Health Records.Cancer nursing · 2026Article
- Performance of Predefined Search Patterns for Identifying Documented Goals-of-Care Discussions in Inpatient Electronic Health Records.Journal of palliative medicine · 2025Article
- Predictors of Concordance between Patient-Reported and Provider-Documented Symptoms in the Context of Cancer and Multimorbidity.Applied clinical informatics · 2024Article
- Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity.JAMIA open · 2024Article
- Symptom-BERT: Enhancing Cancer Symptom Detection in EHR Clinical Notes.Journal of pain and symptom management · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeIdentifying cancer symptoms in electronic health record (EHR) narratives is feasible with natural language processing (NLP). However, more efficient NLP systems are needed to detect various symptoms and distinguish observed symptoms from negated symptoms and medication-related side effects. We evaluated the accuracy of NLP in (1) detecting 14 symptom groups (ie, pain, fatigue, swelling, depressed mood, anxiety, nausea/vomiting, pruritus, headache, shortness of breath, constipation, numbness/tingling, decreased appetite, impaired memory, disturbed sleep) and (2) distinguishing observed symptoms in EHR narratives among patients with cancer.
methodsWe extracted 902,508 notes for 11,784 unique patients diagnosed with cancer and developed a gold standard corpus of 1,112 notes labeled for presence or absence of 14 symptom groups. We trained an embeddings-augmented NLP system integrating human and machine intelligence and conventional machine learning algorithms. NLP metrics were calculated on a gold standard corpus subset for testing.
resultsThe interannotator agreement for labeling the gold standard corpus was excellent at 92%. The embeddings-augmented NLP model achieved the best performance (F1 score = 0.877). The highest NLP accuracy was observed in pruritus (F1 score = 0.937) while the lowest accuracy was in swelling (F1 score = 0.787). After classifying the entire data set with embeddings-augmented NLP, we found that 41% of the notes included symptom documentation. Pain was the most documented symptom (29% of all notes) while impaired memory was the least documented (0.7% of all notes).
conclusionWe illustrated the feasibility of detecting 14 symptom groups in EHR narratives and showed that an embeddings-augmented NLP system outperforms conventional machine learning algorithms in detecting symptom information and differentiating observed symptoms from negated symptoms and medication-related side effects.
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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.