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Relationship Between Syndromes and Prescriptions of Damp Disease: a Neural Network-based Study on Cases from Lin Zheng Zhi Nan Yi An Shi / 中国中医药信息杂志
Chinese Journal of Information on Traditional Chinese Medicine ; (12): 91-95, 2017.
Article in Chinese | WPRIM | ID: wpr-614175
ABSTRACT
Objective Taking medical cases in Lin Zheng Zhi Nan Yi An Shi as examples to analyze the network relationship between the syndromes and prescriptions through building a medical case model forecasting medication via artificial neural networks for the syndromes and prescriptions in medical cases. Methods The study screened medical cases in Lin Zheng Zhi Nan Yi An Shi, and standardized and entered the data with Python language programming. PyBrain module was used to build and train a network model. The MatPlotLib module drew the error curve and the predicted fit curve, and evaluated the sensitivity and specificity. NetworkX module realized the visual expression of the network relationship between the syndromes and prescriptions, and analyzed the medicine within the prescriptions and compatibility relationship and the relationship between the pathogenesis and pathology. Results The sensitivity of the constructed medical case network model was 96.15% and the specificity was 75.00%. The visual mapping of the network relationship between the syndromes and prescriptions and the analysis on single, single group, and multi-angle were realized. Conclusion Neural network is capable to simulate the relationship between syndromes and prescriptions of medical knowledge. The visual combination and manifestation of network can provide a feasible solution for the knowledge discovery in medical literature.

Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Journal of Information on Traditional Chinese Medicine Year: 2017 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Type of study: Prognostic study Language: Chinese Journal: Chinese Journal of Information on Traditional Chinese Medicine Year: 2017 Type: Article