Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival
- Authors
- Kim, Sunkyu; Lee, Choong-kun; Choi, Yonghwa; Baek, Eun Sil; Choi, Jeong Eun; Lim, Joon Seok; Kang, Jaewoo; Shin, Sang Joon
- Issue Date
- 17-11월-2021
- Publisher
- FRONTIERS MEDIA SA
- Keywords
- MRI; deep learning; natural language processing (NLP); rectal cancer; survival prediction
- Citation
- FRONTIERS IN ONCOLOGY, v.11
- Indexed
- SCIE
SCOPUS
- Journal Title
- FRONTIERS IN ONCOLOGY
- Volume
- 11
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/136675
- DOI
- 10.3389/fonc.2021.747250
- ISSN
- 2234-943X
- Abstract
- Most electronic medical records, such as free-text radiological reports, are unstructured; however, the methodological approaches to analyzing these accumulating unstructured records are limited. This article proposes a deep-transfer-learning-based natural language processing model that analyzes serial magnetic resonance imaging reports of rectal cancer patients and predicts their overall survival. To evaluate the model, a retrospective cohort study of 4,338 rectal cancer patients was conducted. The experimental results revealed that the proposed model utilizing pre-trained clinical linguistic knowledge could predict the overall survival of patients without any structured information and was superior to the carcinoembryonic antigen in predicting survival. The deep-transfer-learning model using free-text radiological reports can predict the survival of patients with rectal cancer, thereby increasing the utility of unstructured medical big data.
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Collections - Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
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