Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11020984 | International Journal of Medical Informatics | 2018 | 37 Pages |
Abstract
This study demonstrates that hierarchically-structured medical knowledge can be incorporated into statistical models, and produces improved performance during automated clinical coding. This performance improvement results primarily from improved representation of rarer diseases. We also show that recurrent neural networks improve representation of medical text in some settings. Learning good representations of the very rare diseases in clinical coding ontologies from data alone remains challenging, and alternative means of representing these diseases will form a major focus of future work on automated clinical coding.
Keywords
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Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Finneas Catling, Georgios P. Spithourakis, Sebastian Riedel,