Distributed representation of patients and its use for medical cost prediction
This work addresses the need for scalable and automated patient representation in healthcare, offering a method that reduces reliance on resource-intensive expert systems, though it is incremental in applying unsupervised learning to a specific domain.
The paper tackles the problem of learning patient representations from medical claims data without expert knowledge, proposing a novel architecture that achieves superior performance compared to existing methods, including a commercial model, in medical cost prediction tasks.
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