Cited 2 times in
Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival
DC Field | Value | Language |
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dc.contributor.author | 신상준 | - |
dc.contributor.author | 이충근 | - |
dc.contributor.author | 임준석 | - |
dc.date.accessioned | 2022-05-09T16:45:41Z | - |
dc.date.available | 2022-05-09T16:45:41Z | - |
dc.date.issued | 2021-11 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/188179 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Frontiers Research Foundation | - |
dc.relation.isPartOf | FRONTIERS IN ONCOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Sunkyu Kim | - |
dc.contributor.googleauthor | Choong-Kun Lee | - |
dc.contributor.googleauthor | Yonghwa Choi | - |
dc.contributor.googleauthor | Eun Sil Baek | - |
dc.contributor.googleauthor | Jeong Eun Choi | - |
dc.contributor.googleauthor | Joon Seok Lim | - |
dc.contributor.googleauthor | Jaewoo Kang | - |
dc.contributor.googleauthor | Sang Joon Shin | - |
dc.identifier.doi | 10.3389/fonc.2021.747250 | - |
dc.contributor.localId | A02105 | - |
dc.contributor.localId | A03259 | - |
dc.contributor.localId | A03408 | - |
dc.relation.journalcode | J03512 | - |
dc.identifier.eissn | 2234-943X | - |
dc.identifier.pmid | 34868947 | - |
dc.subject.keyword | MRI | - |
dc.subject.keyword | deep learning | - |
dc.subject.keyword | natural language processing (NLP) | - |
dc.subject.keyword | rectal cancer | - |
dc.subject.keyword | survival prediction | - |
dc.contributor.alternativeName | Shin, Sang Joon | - |
dc.contributor.affiliatedAuthor | 신상준 | - |
dc.contributor.affiliatedAuthor | 이충근 | - |
dc.contributor.affiliatedAuthor | 임준석 | - |
dc.citation.volume | 11 | - |
dc.citation.startPage | 747250 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN ONCOLOGY, Vol.11 : 747250, 2021-11 | - |
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