  Efficient representation of patients is very important in the healthcare
domain and can help with many tasks such as medical risk prediction. Many
existing methods, such as diagnostic Cost Groups (DCG), rely on expert
knowledge to build patient representation from medical data, which is resource
consuming and non-scalable. Unsupervised machine learning algorithms are a good
choice for automating the representation learning process. However, there is
very little research focusing on onpatient-level representation learning
directly from medical claims. In this paper, weproposed a novel patient vector
learning architecture that learns high quality,fixed-length patient
representation from claims data. We conducted several experiments to test the
quality of our learned representation, and the empirical results show that our
learned patient vectors are superior to vectors learned through other methods
including a popular commercial model. Lastly, we provide potential clinical
interpretation for using our representation on predictive tasks, as
interpretability is vital in the healthcare domain
