Concept Embedding for Relevance Detection of Search Queries Regarding CHOP.

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.

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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)

Division/Institute:

Department of Engineering and Information Technology
Department of Engineering and Information Technology > Institute for Medical Informatics

Name:

Deng, Yihan; Faulstich, Lukas and Denecke, Kerstin

ISSN:

1879-8365

Language:

English

Submitter:

Kerstin Denecke

Date Deposited:

25 Mar 2020 10:52

Last Modified:

25 Mar 2020 10:52

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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