ArticleJAMIA open2020
Natural language processing for abstraction of cancer treatment toxicities: accuracy versus human experts.
Article in JAMIA open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
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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
26 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review.JMIR medical informatics · 2025Pooled it
- Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review.Expert review of pharmacoeconomics & outcomes research · 2024Pooled it
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Harmonisation of large-scale, heterogeneous individual participant adverse event data from randomised trials of statin therapy.Clinical trials (London, England) · 2022Pooled it
- Review
- Automated extraction of fluoropyrimidine treatment and treatment-related toxicities from clinical notes using natural language processing.International journal of medical informatics · 2026Article
- Natural language processing to identify documented pain preceding radiation therapy for bone metastases.JNCI cancer spectrum · 2026Article
- From BERT to GPT-4: A systematic review of AI-Driven toxicity extraction and grading in radiation oncology.Technical innovations & patient support in radiation oncology · 2026Article
- Large Language Models in Population Oncology: A Contemporary Review on the Use of Large Language Models to Support Data Collection, Aggregation, and Analysis in Cancer Care and Research.JCO clinical cancer informatics · 2025Review
- Real-time data in cancer registries: Validation of an automated data extraction system.iScience · 2025Article
- Using Natural Language Processing to Identify Symptomatic Adverse Events in Pediatric Oncology: Tutorial for Clinician Researchers.JMIR bioinformatics and biotechnology · 2025Article
- Patterns in Symptoms Preceding Acute Care in Patients With Cancer.JAMA network open · 2025Article
- Integrating natural language processing into radiation oncology: a practical guide to transformer architecture and large language models.BJR artificial intelligence · 2025Review
- Natural Language Processing Accurately Differentiates Cancer Symptom Information in Electronic Health Record Narratives.JCO clinical cancer informatics · 2024Article
- Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.JCO clinical cancer informatics · 2024Article
- Promise and Perils of Large Language Models for Cancer Survivorship and Supportive Care.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- Information heterogeneity between progress notes by physicians and nurses for inpatients with digestive system diseases.Scientific reports · 2024Article
- Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways.Pharmacogenomics and personalized medicine · 2024Review
- Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology.JCO clinical cancer informatics · 2023Article
- Development of a keyword library for capturing PRO-CTCAE-focused "symptom talk" in oncology conversations.JAMIA open · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesExpert abstraction of acute toxicities is critical in oncology research but is labor-intensive and variable. We assessed the accuracy of a natural language processing (NLP) pipeline to extract symptoms from clinical notes compared to physicians. MATERIALS AND
methodsTwo independent reviewers identified present and negated National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 symptoms from 100 randomly selected notes for on-treatment visits during radiation therapy with adjudication by a third reviewer. A NLP pipeline based on Apache clinical Text Analysis Knowledge Extraction System was developed and used to extract CTCAE terms. Accuracy was assessed by precision, recall, and F1.
resultsThe NLP pipeline demonstrated high accuracy for common physician-abstracted symptoms, such as radiation dermatitis (F1 0.88), fatigue (0.85), and nausea (0.88). NLP had poor sensitivity for negated symptoms.
conclusionNLP accurately detects a subset of documented present CTCAE symptoms, though is limited for negated symptoms. It may facilitate strategies to more consistently identify toxicities during cancer therapy.
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