ArticleJCO clinical cancer informatics2022
RUBY: Natural Language Processing of French Electronic Medical Records for Breast Cancer Research.
Article in JCO clinical cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Review
- 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
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Anticancer Drug Development-Keep a Cool Head.Pharmaceutics · 2024Article
- Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area.Yearbook of medical informatics · 2023Article
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Authors and funding
11 authors.
Funding
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Abstract
purposeElectronic medical records are a valuable source of information about patients' clinical status but are often free-text documents that require laborious manual review to be exploited. Techniques from computer science have been investigated, but the literature has marginally focused on non-English language texts. We developed RUBY, a tool designed in collaboration with IBM-France to automatically structure clinical information from French medical records of patients with breast cancer. MATERIALS AND
methodsRUBY, which exploits state-of-the-art Named Entity Recognition models combined with keyword extraction and postprocessing rules, was applied on clinical texts. We investigated the precision of RUBY in extracting the target information.
resultsRUBY has an average precision of 92.8% for the Surgery report, 92.7% for the Pathology report, 98.1% for the Biopsy report, and 81.8% for the Consultation report.
conclusionThese results show that the automatic approach has the potential to effectively extract clinical knowledge from an extensive set of electronic medical records, reducing the manual effort required and saving a significant amount of time. A deeper semantic analysis and further understanding of the context in the text, as well as training on a larger and more recent set of reports, including those containing highly variable entities and the use of ontologies, could further improve the results.
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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.