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ArXiv Preprint

Healthcare representation learning on the Electronic Health Record (EHR) is seen as crucial for predictive analytics in the medical field. Many natural language processing techniques, such as word2vec, RNN and self-attention, have been adapted for use in hierarchical and time stamped EHR data, but fail when they lack either general or task-specific data. Hence, some recent works train healthcare representations by incorporating medical ontology (a.k.a. knowledge graph), by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are never exploited to enhance ontology learning. To address this, we propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics. Specifically, it consists of task-specific representation learning and graph-embedding modules to learn both patient journey and medical ontology interactively. Consequently, this creates a mutual integration to benefit both healthcare representation learning and medical ontology embedding. Moreover, such integration is achieved by a joint training of both task-specific predictive and ontology-based disease typing tasks based on fused embeddings of the two modules. Experiments conducted on two real-world diagnosis prediction datasets show that, our healthcare representation model MIMO not only achieves better predictive results than previous state-of-the-art approaches regardless of sufficient or insufficient training data, but also derives more interpretable embeddings of diagnoses.

Xueping Peng, and Guodong Long, Tao Shen, Sen Wang, Zhendong Niu, Chengqi Zhang