Chinese Patent Medicine Recommendation Algorithm Based on DPCNN-DeepFM

EasyChair Preprint 11075

Abstract

In this paper, we combine the embedded deep pyramid network and the deep FM recommendation model to complete the recommendation of proprietary Chinese medicines. Using real electronic medical records provided by Beijing University of Chinese Medicine and the Institute of Computer Science, Chinese Academy of Sciences as data sources; according to the patient’s main complaint, symptoms, tongue, pulse, age, gender, and other characteristics; using a deep pyramid network based on embedded patient feature fusion to predict the patient’s card Candidate categories, realize long-distance text association through text region embedding and fixed number of feature maps; and use deep FM model to process the sparse and dense features of the patient with the FM layer and the DNN layer separately, and finally real-time features and predictions for the patient The syndrome types are sorted. After comparative experiments and training, the AUC index is 78.90%, and the ACC value is 83.26%. Its overall performance is better than other RNN, CNN, and fast text network models. The experimental results show that the recommendation algorithm of Chinese patent medicine based on DPCNN-DeepFM has certain reference significance for the auxiliary clinical diagnosis of patients.

Keyphrases: Chinese patent medicine, DPCNN, DeepFM, Recommendation, Recommendation System, TCM diagnosis

@booklet{EasyChair:11075,
  author    = {Yunpeng Huang and Huihua Yang and Weiguang Wang and Feng Li and Xiaofeng Liu and Jiahui Ouyang},
  title     = {Chinese Patent Medicine Recommendation Algorithm Based on DPCNN-DeepFM},
  howpublished = {EasyChair Preprint 11075},
  year      = {EasyChair, 2023}}