School of Software, Henan University, Kaifeng, China.
School of Digital Arts and Communication, Shandong University of Art & Design, Jinan, China.
PLoS One. 2023 Mar 16;18(3):e0282824. doi: 10.1371/journal.pone.0282824. eCollection 2023.
Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.
最近，很多中国患者通过社交网络平台咨询治疗方案，但中文医疗文本包含丰富的信息，包括大量的医学术语和症状描述。如何构建一个智能模型，自动对患者咨询的文本信息进行分类，并为患者推荐正确的科室，这非常重要。为了解决中文医疗文本特征提取不足、准确率低的问题，本文提出了一种双通道中文医疗文本分类模型。该模型在不同粒度上提取中文医疗文本的特征，全面准确地获取有效的特征信息，最后根据文本分类为患者推荐科室。模型的一个通道侧重于与医院科室相关的医学术语、症状等词汇，给予不同的权重，用不同大小的卷积核计算相应的特征向量，从而得到局部文本表示。另一个通道使用 BiGRU 网络和注意力机制获取文本表示，突出整句的重要信息，即全局文本表示。最后，模型使用全连接层将两个通道的表示向量进行组合，并使用 Softmax 分类器进行分类。实验结果表明，与基线模型相比，该模型在平均准确率、召回率和 F1 分数上分别提高了 10.65%、8.94%和 11.62%，证明了我们的模型具有更好的性能和鲁棒性。
