RDKG: A Reinforcement Learning Framework for Disease Diagnosis on Knowledge Graph
Sponsor
This work was supported by the National Key Research and Development Plan of China (No. 2022YFF0712200), Beijing Natural Science Foundation under Grant No. 4212030, Science and Technology Service Network Initiative, Chinese Academy of Sciences (No. KFJ-STS-QYZD-2021-11-001) and Youth Innovation Promotion Association CAS.
Published In
2023 IEEE International Conference on Data Mining (ICDM)
Document Type
Citation
Publication Date
2024
Abstract
Automatic disease diagnosis from symptoms has attracted much attention in medical practices. It can assist doctors and medical practitioners in narrowing down disease candidates, reducing testing costs, improving diagnosis efficiency, and more importantly, saving human lives. Existing research has made significant progress in diagnosing disease but was limited by the gap between interpretability and accuracy. To fill this gap, in this paper, we propose a method called Reinforced Disease Diagnosis on Knowlege Graph (RDKG). Specifically, we first construct a knowledge graph containing all information from electronic medical records. To capture informative embeddings, we propose an enhanced knowledge graph embedding method that can embed information outside the knowledge graph into entity embedding. Then we transform the automatic disease diagnosis task into a Markov decision process on the knowledge graph. After that, we design a reinforcement learning method with a soft reward mechanism and a pruning strategy to solve the Markov decision process. We accomplish automated disease diagnosis by finding a path from symptoms to disease. The experimental results show that our model can effectively utilize heterogeneous information in the knowledge graph to complete the automatic disease diagnosis. Besides, our model demonstrates supreme performance in both accuracy and interpretability.
Rights
© 2023 IEEE.
Locate the Document
DOI
10.1109/ICDM58522.2023.00122
Persistent Identifier
https://archives.pdx.edu/ds/psu/41730
Publisher
IEEE
Citation Details
Guo, S., Liu, K., Wang, P., Dai, W., Du, Y., Zhou, Y., & Cui, W. (2023, December). RDKG: A Reinforcement Learning Framework for Disease Diagnosis on Knowledge Graph. In 2023 IEEE International Conference on Data Mining (ICDM) (pp. 1049-1054). IEEE.