Multi-relational EHR representation learning with infusing information of diagnosis and medication

Medical concept embedding which aims at learning interpretable low-dimensional representations of medical codes has become one of the key technologies to enable the machine (deep) learning models to imitate the doctor’s cognitive reasoning process in a variety of clinical tasks. Most existing works focus on leveraging the medical ontology to get the representations but remains ineffective in dealing with 1) the inconsistency between the knowledge of the medical ontology and the observations in health records, and 2) the deficiency of discovering the relations among multi-types of medical concepts. To address these challenges, this paper proposes MrER(Multi-relational EHR representation learning method). It’s a heterogeneous graph convolutional network with a self-adaptive adjacency matrix, to infer the multi-relations among different types of medical concepts and align them in the same subspace for the complex knowledge inference. Moreover, an temporal convolutional network is introduced to capture the dependency patterns in the sequence of medical records. The entire framework is trained in an end-to-end fashion. The experimental results show that MrER achieves competitive performance advantages in sequential diagnosis prediction task in comparison with state-of-the-art methods and the learned embeddings have good interpretability regarding the relationship between medical codes.