ArticlePatterns (New York, N.Y.)2023
Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- From electronic health records to real-world evidence: Savana Next-Generation Registry digital solution.ESMO real world data and digital oncology · 2026Article
- Design and evaluation of an automated pediatric acute lymphoblastic leukemia registry from clinical data warehouses.BMC medical informatics and decision making · 2026Article
- Development and Assessment of a Pipeline for Extracting Structured Data From Free-Text Medical Reports Using a Large Language Model.JCO clinical cancer informatics · 2026Article
- High-precision information retrieval for rapid clinical guideline updates.NPJ digital medicine · 2025Article
- Large language models in oncology: a review.BMJ oncology · 2025Review
- Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation.European journal of radiology open · 2024Article
- Generalizable and automated classification of TNM stage from pathology reports with external validation.Nature communications · 2024Article
- Artificial Intelligence in Medical Affairs: A New Paradigm with Novel Opportunities.Pharmaceutical medicine · 2024Review
- Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision.Patterns (New York, N.Y.) · 2023Article
- Addressing the unmet need for a comprehensive lung cancer registry in Romania.Frontiers in oncology · 2023Article
- Data Extraction From Oncology Imaging Reports by Large Language Models: A Comparative Accuracy Study.JCO clinical cancer informaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and time consuming. Developing natural language processing (NLP) methods for structuring RWD is thus essential for scaling real-world evidence generation. We propose leveraging patient-level supervision from medical registries, which are often readily available and capture key patient information, for general RWD applications. We conduct an extensive study on 135,107 patients from the cancer registry of a large integrated delivery network (IDN) comprising healthcare systems in five western US states. Our deep-learning methods attain test area under the receiver operating characteristic curve (AUROC) values of 94%-99% for key tumor attributes and comparable performance on held-out data from separate health systems and states. Ablation results demonstrate the superiority of these advanced deep-learning methods. Error analysis shows that our NLP system sometimes even corrects errors in registrar labels.
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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.