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. 2023 Jan 20;23(1):15.
doi: 10.1186/s12911-023-02105-7.

Automatic medical specialty classification based on patients' description of their symptoms

Chao Mao #  1 , Quanjing Zhu #  2 , Rong Chen  3 , Weifeng Su  4
Affiliations

Automatic medical specialty classification based on patients' description of their symptoms

Chao Mao et al. BMC Med Inform Decis Mak. .

Abstract

In China, patients usually determine their medical specialty before they register the corresponding specialists in the hospitals. This process usually requires a lot of medical knowledge for the patients. As a result, many patients do not register the correct specialty for the first time if they do not receive help from the hospitals. In this study, we try to automatically direct the patients to the appropriate specialty based on the symptoms they described. As far as we know, this is the first study to solve the problem. We propose a neural network-based model based on a hybrid model integrated with an attention mechanism. To prove the actual effect of this hybrid model, we utilized a data set of more than 40,000 items, including eight departments, such as Otorhinolaryngology, Pediatrics, and other common departments. The experiment results show that the hybrid model achieves more than 93.5% accuracy and has a high generalization capacity, which is superior to traditional classification models.

Keywords: Attention; BERT; Convolutional neural network; Medical specialty classification; Registration.

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Conflict of interest statement

The authors have no relevant financial or non-financial interests to disclose.

Figures

Fig. 1
Fig. 1
The overall architecture of the Hybrid methods, HyM, including TF-IDF, BERT, LSTM and TEXT-CNN, are applied to extract the features at different levels from the symptom texts. The features are concatenated and fed into a fully connected neural network for classification
Fig. 2
Fig. 2
LSTM unit structure
Fig. 3
Fig. 3
The confusion matrices (8 × 8) of four different model. A The confusion matrix of TEXT-CNN. B The confusion matrix of LSTM. C The confusion matrix of BERT. D The confusion matrix of HyM. The row indicates the number of data instances belonging to this class, and column of the confusion matrix indicates the number of data instances that has been predicted as this category. The confusion matrix is used to summarize the results of a classifier, where the closer the color is to the dark green in the middle of the picture, the more accurate the model is. From the four aboved pictures, the confusion matrix show that the HyM is superior to traditional classification models (TEXT-CNN, LSTM, BERT). TEXT-CNN, text convolutional neural networks; LSTM, long short-term memory; BERT, bidirectional encoder representations from transformers; HyM, Hybrid Model
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