期刊
APPLIED SCIENCES-BASEL
卷 10, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/app10155262
关键词
ICD coding; hierarchical classification; electronic healthcare
类别
资金
- VLAIO SBO project [150056]
- ERC Advanced Grant CALCULUS H2020-ERC-2017-ADG [788506]
- European Research Council (ERC) [788506] Funding Source: European Research Council (ERC)
In this survey, we discuss the task of automatically classifying medical documents into the taxonomy of the International Classification of Diseases (ICD), by the use of deep neural networks. The literature in this domain covers different techniques. We will assess and compare the performance of those techniques in various settings and investigate which combination leverages the best results. Furthermore, we introduce an hierarchical component that exploits the knowledge of the ICD taxonomy. All methods and their combinations are evaluated on two publicly available datasets that represent ICD-9 and ICD-10 coding, respectively. The evaluation leads to a discussion of the advantages and disadvantages of the models.
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