Exploration of Decision-Making Methods Based on Syndrome Differentiation by “Data-Knowledge” Dual-Driven Models： A Case Study of Gastric Precancerous State / 中医杂志

ABSTRACT

Data analysis models may assist the transmission of traditional Chinese medicine （TCM） experience and clinical diagnosis and treatment， and the possibility of constructing a “data-knowledge” dual-drive model was explored by taking gastric precancerous state as an example. Data-driven is to make clinical decisions around data analysis， and its syndrome-differentiation decision-making research relies on hidden structural models and partially observable Markov decision-making processes to identify the etiology of diseases， syndrome elements， evolution of pathogenesis， and syndrome differentiation protocols； knowledge-driven is to make use of data and information to promote decision-making and action processes， and its syndrome-differentiation decision-making research relies on convolutional neural networks to improve the accuracy of local disease identification and syndrome differentiation. The “data-knowledge” dual-driven model can make up for the shortcomings of single-drive numerical simulation accuracy， and achieve a balance between local disease identification and macroscopic syndrome differentiation. On the basis of previous research， we explored the construction method of diagnostic assisted decision-making platform for gastric precancerous state， and believed that the diagnostic and decision-making ability of doctors can be extended through the assistance of machines and algorithms. Meanwhile， the related research methods were integrated and the core features of gastric precancerous state based on TCM syndrome differentiation and endoscopic pathology diagnosis and prediction were obtained， and the elements of endoscopic pathology recognition based on TCM syndrome differentiation were explored， so as to provide ideas for the in-depth research and innovative application of cutting-edge data analysis technology in the field of intelligent TCM syndrome differentiation.