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...

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Bibliographic Details
Published in:Frontiers in oncology 2021-11, Vol.11, p.747250-747250
Main Authors: Kim, Sunkyu, Lee, Choong-kun, Choi, Yonghwa, Baek, Eun Sil, Choi, Jeong Eun, Lim, Joon Seok, Kang, Jaewoo, Shin, Sang Joon
Format: Article
Language:English
Subjects:
deep learning
MRI
natural language processing (NLP)
Oncology
rectal cancer
survival prediction
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Summary: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.
ISSN:2234-943X
2234-943X
DOI:10.3389/fonc.2021.747250