Distributed representation of patients and its use for medical cost prediction

TL;DR

This paper introduces a novel unsupervised method for learning fixed-length patient representations from medical claims data, outperforming existing models and enabling better predictive healthcare tasks with interpretability.

Contribution

The paper presents a new patient vector learning architecture that directly derives high-quality representations from claims data, improving over existing methods including commercial models.

Findings

Learned patient vectors outperform other methods in quality.

Representations enable improved predictive healthcare tasks.

Potential for clinical interpretability of patient vectors.

Abstract

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