Journal
ELECTRONICS
Volume 11, Issue 14, Pages -Publisher
MDPI
DOI: 10.3390/electronics11142229
Keywords
medical record; text classification; capsule network
Categories
Funding
- National Natural Science Foundation of China [62073123]
- Major Public Welfare Project of Henan Province [201300311200]
Ask authors/readers for more resources
This paper proposes a Capsule network model for electronic medical record classification in Chinese. The model combines LSTM and GRU models and utilizes a unique routing structure to extract complex Chinese medical text features.
In the medical field, text classification based on natural language process (NLP) has shown good results and has great practical application prospects such as clinical medical value, but most existing research focuses on English electronic medical record data, and there is less research on the natural language processing task for Chinese electronic medical records. Most of the current Chinese electronic medical records are non-institutionalized texts, which generally have low utilization rates and inconsistent terminology, often mingling patients' symptoms, medications, diagnoses, and other essential information. In this paper, we propose a Capsule network model for electronic medical record classification, which combines LSTM and GRU models and relies on a unique routing structure to extract complex Chinese medical text features. The experimental results show that this model outperforms several other baseline models and achieves excellent results with an F1 value of 73.51% on the Chinese electronic medical record dataset, at least 4.1% better than other baseline models.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available