An accurate diagnostic model for PNS in children based on random wandering and vector space Constructing research^*

Pediatric nephrotic syndrome (PNS) is a common urological disease in children, and one of the main symptoms of PNS is spleen and kidney yang deficiency. The aim of this study was to construct a 5-layer accurate diagnostic model, adding disease staging and syndromic grouping to the three diagnostic layers of "disease, evidence and symptom". First, we use the random walk method for the topological network of primary symptoms and the feature group similarity calculation method for secondary symptoms to match the optimal staging of cases. Then, we use the cosine similarity between the vector space of each evidence grouping and the case ensemble vector to select the optimal evidence grouping. This approach provides new ideas and methods for clinical individualized treatment in traditional Chinese medicine (TCM).