JLAN: medical code prediction via joint learning attention networks and denoising mechanism

JLAN: medical code prediction via joint learning attention networks and denoising mechanism

Background: Clinical notes are documents that contain detailed information about the health status of patients. Medical codes generally accompany them. However, the manual diagnosis is costly and error-prone. Moreover, large datasets in clinical diagnosis are susceptible to noise labels because of e...

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Bibliographic Details
Main Authors: Dong, D. (Author), Islam, F. (Author), Li, X. (Author), Lu, M. (Author), Wei, H. (Author), Zhang, Y. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
ART model
Attention mechanism
Attention mechanisms
Automatic diagnosis
Classification (of information)
Clinical notes
Code predictions
Codes (symbols)
Computer aided diagnosis
Convolutional neural networks
De-noising
Denoising model
electronic health record
Electronic Health Records
Forecasting
Health status
human
Humans
International Classification of Diseases
Joint learning
Large dataset
Learning systems
machine learning
Machine Learning
Multi-label classification
Network coding
Neural Networks, Computer
State of the art
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