Deng, Yihan; Faulstich, Lukas; Denecke, Kerstin (2017). Concept Embedding for Relevance Detection of Search Queries Regarding CHOP. Studies in Health Technology and Informatics, 245, p. 1260. IOS Press
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Concept Embedding for Relevance Detection of search queries regarding CHOP.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial (CC-BY-NC). Download (147kB) | Preview |
Automatic encoding of diagnosis and procedures can increase the interoperability and efficacy of the clinical cooperation. The concept, rule-based and machine learning classification methods for automatic code generation can easily reach their limit due to the handcrafted rules and a limited coverage of the vocabulary in a concept library. As the first step to apply deep learning methods in automatic encoding in the clinical domain, a suitable semantic representation should be generated. In this work, we will focus on the embedding mechanism and dimensional reduction method for text representation, which mitigate the sparseness of the data input in the clinical domain. Different methods such as word embedding and random projection will be evaluated based on logs of query-document matching.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
School of Engineering and Computer Science > Institute for Patient-centered Digital Health School of Engineering and Computer Science |
Name: |
Deng, Yihan; Faulstich, Lukas and Denecke, Kerstin  0000-0001-6691-396X |
ISSN: |
1879-8365 |
Publisher: |
IOS Press |
Language: |
English |
Submitter: |
Kerstin Denecke |
Date Deposited: |
25 Mar 2020 10:52 |
Last Modified: |
15 Jan 2024 15:30 |
PubMed ID: |
29295345 |
Uncontrolled Keywords: |
Automatic Encoding Classification Machine Learning |
ARBOR DOI: |
10.24451/arbor.9178 |
URI: |
https://arbor.bfh.ch/id/eprint/9178 |
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