Pre-screening Textual Based Evaluation for the Diagnosed Female Breast Cancer (WBC)

The existing virtual assistants (VAs) for medical services cannot output satisfactory results on Chinese language processing (CLP). This paper attempts to design a VA that identifies the seriousness and improves the awareness of breast cancer (BC) based on inputs of Chinese texts. Our VA was developed based on the neural network called long short-term memory (LSTM), integrating two N-gram models, namely, bigram and trigram. The integrated models are critical to text-based Chinese word segmentation (CWS). The sequence-to-sequence learning was introduced to covert the CWS into a framework of sequence classification. The proposed VA was compared with several state-of-the-art methods through an experiment. The results show that our method achieved a high accuracy (94%~97%) in identifying the high-frequency characters. The research findings are helpful to the BC identification of Chinese women.