Deep learning approach to detection of colonoscopic information from unstructured reports
Open accessAbstract
BACKGROUND: Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of unstructured text data are still insufficient despite the large amounts of accumulated data. We aimed to develop and apply deep learning-based natural language processing (NLP) methods to detect colonoscopic information. METHODS: This study applied several deep learning-based NLP models to colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared the long short-term memory (LSTM) and BioBERT model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using large unlabeled data (280,668 reports) to the selected model. RESULTS: The NLP model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 scores for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers. CONCLUSIONS: This study applied deep learning-based clinical NLP models to extract meaningful information from colonoscopy reports. The method in this study achieved promising results that demonstrate it can be applied to various practical purposes.
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Our review highlighted the generalized performance of LLM-enabled data extraction across various text types in oncology, including histological and pathological classification, imaging report classification, and data extraction from postoperative surgery reports.
“For instance, our review highlighted the generalized performance of LLM-enabled data extraction across various text types in oncology, including histological and pathological classification [936], imaging report classification [814], and data extraction from postoperative surgery reports [5].”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [5]
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Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review
Chen, D., Alnassar, S. A., Avison, K. E., Huang, R. S., & Raman, S. (2025) · JMIR Cancer · doi:10.2196/65984 · Cited by 66 · see citing works
“…transformer models may require less time and fewer resources needed to manually annotate training datasets compared to classical machine learning models and can better address nuanced edge cases in data extraction that may not be explicitly accounted for in rule-based data approaches [5,6].”
- Authors
- Seong, D., Choi, Y. H., Shin, S.-Y., & Yi, B.-K.
- Affiliations
- Sungkyunkwan University (KR)
- Samsung Medical Center (KR)
- Kangwon National University (KR)
- Corresponding author
- Yi, Byoung-Kee
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2023-02-07
- Volume
- 23
- Issue
- 1
- Pages
- 28-28
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC9903463
- OpenAlex
- W4319437616
- Cited by
- 16
- References
- 61
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 2 · 2023: 1
- FWCI
- 1.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Health and Welfare (HI19C1328)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- clinical text, natural language processing, bidirectional LSTM, conditional random fields, BioBERT, pre-trained word embeddings, colorectal cancer screening, colorectal polyps, clinical information extraction
- MeSH terms
- Data Warehousing, Deep Learning, Colonoscopy, Humans, Natural Language Processing, Colorectal Neoplasms
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