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 | Kim, Sunkyu | - |
dc.contributor.author | Lee, Choong-kun | - |
dc.contributor.author | Choi, Yonghwa | - |
dc.contributor.author | Baek, Eun Sil | - |
dc.contributor.author | Choi, Jeong Eun | - |
dc.contributor.author | Lim, Joon Seok | - |
dc.contributor.author | Kang, Jaewoo | - |
dc.contributor.author | Shin, Sang Joon | - |
dc.date.accessioned | 2022-02-23T23:40:33Z | - |
dc.date.available | 2022-02-23T23:40:33Z | - |
dc.date.created | 2022-02-15 | - |
dc.date.issued | 2021-11-17 | - |
dc.identifier.issn | 2234-943X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/136675 | - |
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.language | English | - |
dc.language.iso | en | - |
dc.publisher | FRONTIERS MEDIA SA | - |
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.affiliatedAuthor | Kang, Jaewoo | - |
dc.identifier.doi | 10.3389/fonc.2021.747250 | - |
dc.identifier.scopusid | 2-s2.0-85120708361 | - |
dc.identifier.wosid | 000726135300001 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN ONCOLOGY, v.11 | - |
dc.relation.isPartOf | FRONTIERS IN ONCOLOGY | - |
dc.citation.title | FRONTIERS IN ONCOLOGY | - |
dc.citation.volume | 11 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Oncology | - |
dc.relation.journalWebOfScienceCategory | Oncology | - |
dc.subject.keywordAuthor | MRI | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | natural language processing (NLP) | - |
dc.subject.keywordAuthor | rectal cancer | - |
dc.subject.keywordAuthor | survival prediction | - |
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