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Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival
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 ser...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635726/ https://www.ncbi.nlm.nih.gov/pubmed/34868947 http://dx.doi.org/10.3389/fonc.2021.747250 |
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author | Kim, Sunkyu Lee, Choong-kun Choi, Yonghwa Baek, Eun Sil Choi, Jeong Eun Lim, Joon Seok Kang, Jaewoo Shin, Sang Joon |
author_facet | Kim, Sunkyu Lee, Choong-kun Choi, Yonghwa Baek, Eun Sil Choi, Jeong Eun Lim, Joon Seok Kang, Jaewoo Shin, Sang Joon |
author_sort | Kim, Sunkyu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8635726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86357262021-12-02 Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival Kim, Sunkyu Lee, Choong-kun Choi, Yonghwa Baek, Eun Sil Choi, Jeong Eun Lim, Joon Seok Kang, Jaewoo Shin, Sang Joon Front Oncol Oncology 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. Frontiers Media S.A. 2021-11-17 /pmc/articles/PMC8635726/ /pubmed/34868947 http://dx.doi.org/10.3389/fonc.2021.747250 Text en Copyright © 2021 Kim, Lee, Choi, Baek, Choi, Lim, Kang and Shin https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Kim, Sunkyu Lee, Choong-kun Choi, Yonghwa Baek, Eun Sil Choi, Jeong Eun Lim, Joon Seok Kang, Jaewoo Shin, Sang Joon Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival |
title | Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival |
title_full | Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival |
title_fullStr | Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival |
title_full_unstemmed | Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival |
title_short | Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival |
title_sort | deep-learning-based natural language processing of serial free-text radiological reports for predicting rectal cancer patient survival |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635726/ https://www.ncbi.nlm.nih.gov/pubmed/34868947 http://dx.doi.org/10.3389/fonc.2021.747250 |
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