Deep Knowledge Reasoning guided Disease Prediction

Disease prediction, which aims to predict possible future diseases for patients, is a fundamental research problem in medical informatics. Many studies have proposed the introduction of external knowledge to enhance deep learning models which have achieved good results, but as most of these studies only consider using single-hop relationship information from knowledge graphs or simply introduce partial knowledge graphs, fail to effectively mine knowledge paths to understand the relationships between diseases. To this end, we propose a new approach, which uses existing medical knowledge graphs for multi-hop reasoning to guide the self-attention based transformer model for disease prediction. Specifically, we design a reinforcement learning algorithm to perform path reasoning in the knowledge graph to obtain explicit disease progression paths and fusion them with the original Electronic Health Records (EHR) data. After embedding we capture the implicit relationships between the deep knowledge and the original EHR information through several transformer encoders based on the self-attention mechanism to better extract features. At the same time, multi-hop knowledge is more interpretable than single-hop knowledge in terms of disease prediction. Experimental results on the real-world medical dataset MIMIC-III show the superiority of the proposed approach compared to a series of state-of-the-art baselines.
^1
1
The code has been uploaded to https://github.com/15536385781/DKRDP