Knowledge Graph Enhanced Transformers for Diagnosis Generation of Chinese Medicine

Chinese medicine(CM)diagnosis intellectualization is one of the hotspots in the research of CM modernization.The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues,however,it is difficult to solve the problems such as excessive or similar categories.With the development of natural language processing techniques,text generation technique has become increasingly mature.In this study,we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues.The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory(BILSTM)with Transformer as the backbone network.Meanwhile,the CM diagnosis generation model Knowledge Graph Enhanced Transformer(KGET)was established by introducing the knowledge in medical field to enhance the inferential capability.The KGET model was established based on 566 CM case texts,and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence(LSTM-seq2seq),Bidirectional and Auto-Regression Transformer(BART),and Chinese Pre-trained Unbalanced Transformer(CPT),so as to analyze the model manifestations.Finally,the ablation experiments were performed to explore the influence of the optimized part on the KGET model.The results of Bilingual Evaluation Understudy(BLEU),Recall-Oriented Understudy for Gisting Evaluation 1(ROUGE1),ROUGE2 and Edit distance of KGET model were 45.85,73.93,54.59 and 7.12,respectively in this study.Compared with LSTM-seq2seq,BART and CPT models,the KGET model was higher in BLEU,ROUGE1 and ROUGE2 by 6.00–17.09,1.65–9.39 and 0.51–17.62,respectively,and lower in Edit distance by 0.47–3.21.The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance.Additionally,the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results.In conclusion,text generation technology can be effectively applied to CM diagnostic modeling.It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models.CM diagnostic text generation technology has broad application prospects in the future.

From the Cognition of the Sick at Heart to the Cognition of Ghosts: The Knowledge Transformation of Cognitive Psychology in the Late Qing/Republican Era(1895-1949)

In examining the historical changes in cognitive psychology from 1895 to 1949, it is necessary to transform cognitive psychology into cognitive practice. In the late Qing and early Republic era(1895-1919), cognitive practice mainly appeared as the cognition of the sick at heart. Against the background of the late Qing importation of Western learning aimed at securing national salvation, the intellectual class, represented by Tan Sitong,hastily dressed the wound to the traditional Chinese learning of Heaven and man inflicted by late nineteenth century scientific knowledge from Europe and America. They regarded cognition as the understanding gained in the practice of self-cultivation, as affording man a spiritual resting place between Heaven and Earth. By the time of the Republic of China(1920-1949), the main form of cognitive practice had become ghostly cognition.Under the banner of saving the country by means of science, higher education researchers,represented by Lu Zhiwei, explored cognition with the help of such intermediaries as physiological mechanisms and language structure. Men could easily become ghosts trapped in intermediaries when they plunged into the conceptual world in search of truth,as their cognitive practice evolved into the conceptual grasp of ghosts. This ghostly cognition continued into later information processing psychology and was disseminated among the mass of the public. A deeper level of the cognition of the sick at heart remains for future study, on the lengthy road home.