ArticleJCO clinical cancer informatics2021
Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records.
Article in JCO clinical cancer informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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.
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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
25 citing papers in PubMed, 42 citations in OpenAlex.
- AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions.Therapeutic innovation & regulatory science · 2026Review
- Developing the multidisciplinary and multispecialty workforce and the systems needed to sustain high-quality care from diagnosis through long-term survivorship.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Review
- Review
- Intelligent MDT treatment decision making for stage III NSCLC using dual level embedding and three level explanation.Scientific reports · 2026Article
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- NRG Oncology Assessment of Artificial Intelligence for Automatic Treatment Planning in Radiation Therapy Clinical Trials: Present and Future.International journal of radiation oncology, biology, physics · 2025Article
- An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic Clinical Quality Measure Reporting: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- Classifying Stereotactic Radiosurgery Patients by Primary Diagnosis Using Natural Language Processing of Clinical Notes.JCO clinical cancer informatics · 2025Article
- Open-Source Hybrid Large Language Model Integrated System for Extraction of Breast Cancer Treatment Pathway From Free-Text Clinical Notes.JCO clinical cancer informatics · 2025Article
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.JCO clinical cancer informatics · 2024Article
- Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns.JCO clinical cancer informatics · 2024Article
- Article
- DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction.medRxiv : the preprint server for health sciences · 2023Article
- DeepPhe-CR: Natural Language Processing Software Services for Cancer Registrar Case Abstraction.JCO clinical cancer informatics · 2023Article
- An End-to-End Natural Language Processing System for Automatically Extracting Radiation Therapy Events From Clinical Texts.International journal of radiation oncology, biology, physics · 2023Article
- A hybrid system to understand the relations between assessments and plans in progress notes.Journal of biomedical informatics · 2023Article
- Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models.Head & neck · 2023Article
- The Utility of Oncology Information Systems for Prognostic Modelling in Head and Neck Cancer.Journal of medical systems · 2023Article
- Multilingual RECIST classification of radiology reports using supervised learning.Frontiers in digital health · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
purposeKnowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer.
methodsWe used a total number of 4,412 patients with 483,782 clinical notes from the Stanford Cancer Institute Research Database containing patients with nonmetastatic prostate, oropharynx, and esophagus cancer. We trained treatment identification models for each cancer type separately and compared performance of using only structured, only unstructured (
resultsFor prostate cancer, we achieved an f1-score of 0.99 (95% CI, 0.97 to 1.00) for radiation and 1.00 (95% CI, 0.99 to 1.00) for surgery using
conclusionOur results show that treatment identification using free-text clinical notes greatly improves upon the performance using billing codes and simple structured data. The approach can be used for treatment cohort identification and adapted for longitudinal cancer treatment identification.
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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 OpenQuestion graph.