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Article in English | WPRIM | ID: wpr-1010334


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.

Humans , Medicine, Chinese Traditional , Pattern Recognition, Automated , Asian People , Language , Learning
Article in Chinese | WPRIM | ID: wpr-981421


With the advances in medicine, people have deeply understood the complex pathogenesis of diseases. Revealing the mechanism of action and therapeutic effect of drugs from an overall perspective has become the top priority of drug design. However, the traditional drug design methods cannot meet the current needs. In recent years, with the rapid development of systems biology, a variety of new technologies including metabolomics, genomics, and proteomics have been used in drug research and development. As a bridge between traditional pharmaceutical theory and modern science, computer-aided drug design(CADD) can shorten the drug development cycle and improve the success rate of drug design. The application of systems biology and CADD provides a methodological basis and direction for revealing the mechanism and action of drugs from an overall perspective. This paper introduces the research and application of systems biology in CADD from different perspectives and proposes the development direction, providing reference for promoting the application.

Humans , Systems Biology , Drug Design , Drug Development , Genomics , Medicine
Article in Chinese | WPRIM | ID: wpr-888067


The pharmacological effects of Angelicae Sinensis Radix from different producing areas are uneven. Accurate identification of its producing areas by computer vision and machine learning(CVML) is conducive to evaluating the quality of Angelicae Sinensis Radix. This paper collected the high-definition images of Angelicae Sinensis Radix from different producing areas using a digital camera to construct an image database, followed by the extraction of texture features based on the grayscale relationship of adjacent pixels in the image. Then a support vector machine(SVM)-based prediction model for predicting the producing areas of Angelicae Sinensis Radix was built. The experimental results showed that the prediction accuracy reached up to 98.49% under the conditions of the model training set occupying 80%, the test set occupying 20%, and the sampling radius(r) of adjacent pixels being 2. When the training set was set to 10%, the prediction accuracy was still over 93%. Among the three producing areas of Angelicae Sinensis Radix, Huzhu county, Qinghai province exhibited the highest error rate, while Heqing county, Yunnan province the lowest error rate. Angelicae Sinensis Radix from Minxian county, Gansu province and Huzhu county, Qinghai province were both wrongly attributed to Heqing county, Yunnan province, while most of those from Huzhu county, Qinghai province were misjudged as the samples produced in Minxian county, Gansu province. The method designed in this paper enabled the rapid and non-destructive prediction of the producing areas of Angelicae Sinensis Radix, boasting high accuracy and strong stability. There were definite morphological differences between Angelicae Sinensis Radix samples from Minxian county, Gansu province and those from Huzhu county, Qinghai province. The wrongly predicted samples from Minxian county, Gansu province and Huzhu city, Qinghai province shared similar morphological characteristics with those from Heqing county, Yunnan province. Most wrongly predicted samples from Heqing county, Yunnan province were similar to the ones from Minxian county, Gansu province in morphological characteristics.

Angelica sinensis , China , Databases, Factual , Drugs, Chinese Herbal/analysis , Plant Roots/chemistry