Automatic annotation for medical texts based on hidden topic and semantic tree

Medical texts lack quantifiable data structure, thus text keyword model based processing method is not practicable. On the basis of research on latent semantic association between words and keywords tree structure, a semantic analysis model based on latent semantic tree was constructed for medical text data mining. Furthermore, the hidden topic is associated with latent semantic research, and a text processing method was designed based on potential Dirichlet allocation and latent semantic tree model, which can form certain readable automatic annotation according to different types of medical texts. This automatic annotation has lower subjectivity, higher accuracy and readability than the keywords model method. Besides, it can assist medical doctors with text notation and classification, reducing their workload. Program results show that this method can be applied to medical image views and to form diagnosis opinion, patient medical records, produce symptomatic prescription. The semantic matching degree for annotation is 67.7%, and the readability of the text can reach 60.02%.