主 办:北 京 中 医 药 大 学
ISSN 1006-2157 CN 11-3574/R

JOURNAL OF BEIJING UNIVERSITY OF TRADITIONAL CHINESE MEDICINE ›› 2020, Vol. 43 ›› Issue (12): 1034-1041.doi: 10.3969/j.issn.1006-2157.2020.12.010

• TCM Informatics • Previous Articles     Next Articles

Establishment of prostate cancer diagnosis model based on big data of traditional Chinese medicine and graph convolutional network*

Li Peng1,2,3, Luo Aijing1,3#, Min Hui4   

  1. 1 The Third Xiangya Hospital of Central South University, Changsha 410013, China;
    2 School of Informatics, Hunan University of Chinese Medicine, Changsha 410208, China;
    3 CSU Key Laboratory of Medical Information Research of Colleges and Universities in Hunan Province, Changsha 410006, China;
    4 School of Software, Hunan College of Information, Changsha 410200, China
  • Received:2020-06-09 Online:2020-12-30 Published:2021-01-05
  • Contact: Prof. Luo Ajjing, M.D.,Doctoral Supervisor. The Third Xiangya Hospital of Central South University. No.138 Tongzipo Road, Yuelu District, Changsha 410006. E-Mail:lpchs617@csu.edu.cn
  • Supported by:
    National Key Research and Development Program of China (No. 2017YFC1703306), Natural Science Foundation for Young Scientists of Hunan Province (No.2019JJ50543), Natural Science Foundation of Hunan Province (No. 2018JJ2301), Key Project of China Hunan Provincial Science & Technology Department (No. 2017SK2111), General Project of China Hunan Provincial Education Department (No. 19C1318)

Abstract: Objective Diagnosis of prostate cancer based on artificial intelligence technology attracts great academic attention at present. However, most of the existing intelligent diagnostic methods can only collect MRI, CT and other image data for prostate cancer diagnosis, and cannot process such data, resulting in huge limitations and unsatisfactory performance. In order to solve the problem, a prostate cancer diagnosis model based on graph convolutional neural network (PCa-GCN) is proposed in this paper. Methods Firstly, prostate cancer data samples were collected from various hospitals, and then the graph of medical record was constructed based on the preprocessing with the jieba word segmentation, the bag-of-words model and the maximum entropy model. Then the graph of medical record was inputted for GCN to learn the graph embedding representation of characteristics of prostate cancer. Finally, the mapping between such features and prostate cancer was accomplished by logic regression based on sigmoid for accurate diagnosis of prostate cancer. Results Experimental results based on k-fold cross validation showed that the PCa-GCN model is superior to the other diagnostic methods in terms of recall rate and ROC curve. Conclusion PCa-GCN model achieved precise diagnosis of prostate cancer and can provide technical support for prostate cancer data analysis and disease prevention.

Key words: prostate cancer, graph convolutional network, graph of medical record, graph embedding representation, diagnosis, recall rate

CLC Number: 

  • R241