Distributed representation of patients and its use for medical risk adjustment

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 patient-level representation learning directly. In this paper, we proposed a novel patient vector learning architecture that learns high quality, fixed-length patient representation from claims data. In addition, our model can learn meaningful medical visit representation and medical code representation at the same time. 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. We also used our patient vector on a real-world application, and it outperforms 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.

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