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Label-indicator morpheme growth on LSTM for Chinese healthcare question department classification.
- Source :
-
Journal of biomedical informatics [J Biomed Inform] 2018 Jun; Vol. 82, pp. 154-168. Date of Electronic Publication: 2018 Apr 27. - Publication Year :
- 2018
-
Abstract
- Background: Current Chinese medicine has an urgent demand for convenient medical services. When facing a large number of patients, understanding patients' questions automatically and precisely is useful. Different from the high professional medical text, patients' questions contain only a small amount of descriptions regarding the symptoms, and the questions are slightly professional and colloquial.<br />Object: The aim of this paper is to implement a department classification system for patient questions. Patients' questions will be classified into 11 departments, such as surgery and others.<br />Methods: This paper presents a morpheme growth model that enhances the memories of key elements in questions, and later extracts the "label-indicators" and germinates the expansion vectors around them. Finally, the model inputs the expansion vectors into a neural network to assign department labels for patients' questions.<br />Results: All compared methods are validated by experiments on three datasets that are composed of real patient questions. The proposed method has some ability to improve the performance of the classification.<br />Conclusions: The proposed method is effective for the departments classification of patients questions and serves as a useful system for the automatic understanding of patient questions.<br /> (Copyright © 2018 Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1532-0480
- Volume :
- 82
- Database :
- MEDLINE
- Journal :
- Journal of biomedical informatics
- Publication Type :
- Academic Journal
- Accession number :
- 29705197
- Full Text :
- https://doi.org/10.1016/j.jbi.2018.04.011