Joint Medical Ontology Representation Learning for Healthcare Predictions

Healthcare predictions aim at predicting diseases of the next visit to hospital with historical Electronic Health Records (EHR), which is a key research field in personalized healthcare. Previous research has demonstrated that learning meaningful medical ontology representations within the healthcare prediction model can alleviate the data insufficiency problem and thus is beneficial to this task. There are two main pathways of learning medical ontology representations. The first is through pre-defined knowledge graph such as the ICD tree, and the second is through the co-occurrence of diseases within each visit. Majority of existing works formalize their model under only one pathway, and fail to utilize the mutual benefits between them. To exploit these benefits, we propose JMRL, an end-to-end and accurate model for healthcare predictions with Joint Medical ontology Representation Learning. JMRL not only utilizes the joint information from both knowledge graph and co-occurrence statistics, but also make use of the mutual benefits between them in an advanced way with two explicit feedback strategies. Experimental results on the MIMIC-III dataset demonstrate the superiority of our model over all existing state-of-the-art approaches.