ArticleTranslational lung cancer research2021
Real-time autOmatically updated data warehOuse in healThcare (ROOT): an innovative and automated data collection system.
Article in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Advancing paediatric robotic surgery: building the foundation with databases.Journal of robotic surgery · 2026Article
- Long-term comparative effectiveness of tepotinib versus standard of care for MET exon 14 skipping-mutated non-small cell lung cancer.Therapeutic advances in medical oncology · 2026Article
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Continuous multimodal data supply chain and expandable clinical decision support for oncology.NPJ digital medicine · 2025Article
- Article
- Real-World Outcomes of Immunotherapy in Second- or Later-Line Non-Small Cell Lung Cancer with Actionable Genetic Alterations.Cancers · 2023Article
- Trends in Survival Rates of Non-Small Cell Lung Cancer With Use of Molecular Testing and Targeted Therapy in Korea, 2010-2020.JAMA network open · 2023Article
- Patient Identification and Tumor Identification Management: Quality Program in a Cancer Multicentric Clinical Data Warehouse.Cancer informatics · 2023Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe American Society for Clinical Oncology recently launched the minimal common oncology data elements project to facilitate cancer data interoperability. However, clinical data are often unrecorded in an organized way, and converting them into a structured format can be time-consuming. Clinical Data Warehouse (CDW) is a database that consolidates data from different clinical sources. However, the clinical data extracted from this database include not only structured data but also natural language generated during clinical practice. Therefore, applying these data to a clinical study is challenging because they are unstructured, and unformatted to allow essential content to be found. This study determined how best to organize a huge amount of clinical data to evaluate the upper aerodigestive tract cancers' clinical features and outcomes, including cancer of the head and neck, esophagus, lung, thymus, and mesothelioma.
methodsThe Real-time autOmatically updated data warehOuse in healThcare (ROOT) uses six main regions to describe the journey of cancer patients. This study, developed an algorithm optimized for each disease category using natural language processing of unstructured data and data capture of structured data. Data from patients diagnosed at the Samsung Medical Center from 2008-2020 were used.
resultsComprehensive clinical data for 67,617 patients across six tumor types: 28,954 with non-small-cell lung cancer, 2,540 with small-cell lung cancer, 30,035 with head and neck cancer, 4,950 with esophageal cancer, 966 with thymic cancer, and 172 with mesothelioma were collected. Additionally, the results of a longitudinal molecular study, including epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) tests, and next-generation sequencing (NGS), were included. Scattered information was integrated and automatically built up to match the cohort, allowing users to capture the most updated test results and treatment outcomes.
conclusionsThis landmark study documented the successful construction of a real-time updating system for medical big data, based on the CDW program.
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