Limitations of Transformers on Clinical Text Classification

Limitations of Transformers on Clinical Text Classification

Bidirectional Encoder Representations from Transformers (BERT) and BERT-based approaches are the current state-of-the-art in many natural language processing (NLP) tasks; however, their application to document classification on long clinical texts is limited. In this work, we introduce four methods...

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Bibliographic Details
Main Authors: Alawad, M. (Author), Coyle, L. (Author), Doherty, J. (Author), Durbin, E.B (Author), Gao, S. (Author), Gounley, J. (Author), Schaefferkoetter, N. (Author), Stroup, A. (Author), Tourassi, G. (Author), Wu, X.-C (Author), Yoon, H.J (Author), Young, M.T (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
Article
artificial neural network
attention network
BERT
bidirectional encoder representations from transformer
Classification (of information)
clinical text
Clinical text classifications
convolutional neural network
Convolutional neural networks
deep learning
Discharge summary
Document Classification
histology
human
Humans
ICD-9
Information retrieval systems
Input sequence
learning algorithm
machine learning
mathematical model
natural language processing
Natural Language Processing
NAtural language processing
Natural language processing systems
neural networks
Neural Networks, Computer
Pre-training
signal noise ratio
State of the art
text classification
Text processing
Tokenization
Online Access:View Fulltext in Publisher

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