JLAN: medical code prediction via joint learning attention networks and denoising mechanism
JLAN: medical code prediction via joint learning attention networks and denoising mechanism
About this item
Full title
Author / Creator
Li, Xingwang , Zhang, Yijia , Islam, Faiz Ul , Dong, Deshi , Wei, Hao and Lu, Mingyu
Publisher
England: BioMed Central Ltd
Journal title
Language
English
Formats
Publication information
Publisher
England: BioMed Central Ltd
Subjects
More information
Scope and Contents
Contents
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 erroneous manual annotation. Therefore, machine learning has be...
Alternative Titles
Full title
JLAN: medical code prediction via joint learning attention networks and denoising mechanism
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_6e98fa44f570473c8480234ba1b3551a
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_6e98fa44f570473c8480234ba1b3551a
Other Identifiers
ISSN
1471-2105
E-ISSN
1471-2105
DOI
10.1186/s12859-021-04520-x
