Previously submitted to: JMIR Medical Informatics (no longer under consideration since Oct 24, 2021)
Date Submitted: Jul 19, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Constructing Automatic Classification Models for Chinese-language Chief Complaint
ABSTRACT
Background:
Chief complaint is the initial, general, and written description of a patient’s symptoms provided during the hospital intake process. By improving the automatic classification of chief complaint text, the quality and efficiency of patients’ hospital visits can be improved.
Objective:
Using chief complaint data in Chinese from the Information Centre of Jiangsu Commission Health, we built models for automatically detecting the correct treating department and then conducted various tests on those models using machine learning and deep learning.
Methods:
The study tested and compared the performances of the traditional machine learning model of SVM with deep learning models of Bi-LSTM, Bi-LSTM-CRF, At-Bi-LSTM-CRF and Bi-GRU-CRF on the chief complaint text data mainly. It is mainly based on Chinese character expansion model train and test in all traditional machine learning and deep learning models.
Results:
We found that the Bi-LSTM performed better at the chief complaint classification task than the SVM and that the performance difference between the deep learning models constructed is not obvious. The F scores of Bi-LSTM, Bi-LSTM-CRF, At-Bi-LSTM-CRF and Bi-GRU-CRF model built for the experiment effectively reach 88.10, 87.91, 88.14 and 87.98.
Conclusions:
We found that the Bi-LSTM performed better at the chief complaint classification task than the SVM and that the performance difference between the deep learning models constructed is not obvious. The F scores of Bi-LSTM, Bi-LSTM-CRF, At-Bi-LSTM-CRF and Bi-GRU-CRF model built for the experiment effectively reach 88.10, 87.91, 88.14 and 87.98.
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