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Deep-Learning-Based Natural Language Processing of Serial Free-Text Radiological Reports for Predicting Rectal Cancer Patient Survival

Authors :
Sunkyu Kim
Choong-kun Lee
Yonghwa Choi
Eun Sil Baek
Jeong Eun Choi
Joon Seok Lim
Jaewoo Kang
Sang Joon Shin
Source :
Frontiers in Oncology, Vol 11 (2021), Frontiers in Oncology
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

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.

Details

Language :
English
Volume :
11
Database :
OpenAIRE
Journal :
Frontiers in Oncology
Accession number :
edsair.doi.dedup.....da03c7eb9b775620ee952f8691e4d382
Full Text :
https://doi.org/10.3389/fonc.2021.747250/full