Background: Liver cancer is a substantial disease burden in China.
As one of the primary diagnostic tools for detecting liver cancer,
dynamic contrast-enhanced computed tomography provides detailed
evidences for diagnosis that are recorded in free-text radiology
reports. Objective: The aim of our study was to apply a deep
learning model and rule-based natural language processing (NLP)
method to identify evidences for liver cancer diagnosis
automatically. Methods: We proposed a pretrained, fine-tuned BERT
(Bidirectional Encoder Representations from Transformers)-based
BiLSTM-CRF (Bidirectional Long Short-Term Memory-Conditional Random
Field) model to recognize the phrases of APHE (hyperintense
enhancement in the arterial phase) and PDPH (hypointense in the
portal and delayed phases). To identify more essential diagnostic
evidences, we used the traditional rule-based NLP methods for the
extraction of radiological features. APHE, PDPH, and other
extracted radiological features were used to design a
computer-aided liver cancer diagnosis framework by random forest.
Results: The BERT-BiLSTM-CRF predicted the phrases of APHE and PDPH
with an F1 score of 98.40% and 90.67%, respectively. The prediction
model using combined features had a higher performance (F1 score,
88.55%) than those using APHE and PDPH (84.88%) or other extracted
radiological features (83.52%). APHE and PDPH were the top 2
essential features for liver cancer diagnosis. Conclusions: This
work was a comprehensive NLP study, wherein we identified evidences
for the diagnosis of liver cancer from Chinese radiology reports,
considering both clinical knowledge and radiology findings. The
BERT-based deep learning method for the extraction of diagnostic
evidence achieved state-of-the-art performance. The high
performance proves the feasibility of the BERT-BiLSTM-CRF model in
information extraction from Chinese radiology reports. The findings
of our study suggest that the deep learning–based method for
automatically identifying evidences for diagnosis can be extended
to other types of Chinese clinical texts.
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Source: All – Medical Journals
Use of BERT (Bidirectional Encoder Representations from
Transformers)-Based Deep Learning Method for Extracting Evidences
in Chinese Radiology Reports: Development of a Computer-Aided Liver
Cancer Diagnosis Framework