  Risk adjustment has become an increasingly important tool in healthcare. It
has been extensively applied to payment adjustment for health plans to reflect
the expected cost of providing coverage for members. Risk adjustment models are
typically estimated using linear regression, which does not fully exploit the
information in claims data. Moreover, the development of such linear regression
models requires substantial domain expert knowledge and computational effort
for data preprocessing. In this paper, we propose a novel approach for risk
adjustment that uses semantic embeddings to represent patient medical
histories. Embeddings efficiently represent medical concepts learned from
diagnostic, procedure, and prescription codes in patients' medical histories.
This approach substantially reduces the need for feature engineering. Our
results show that models using embeddings had better performance than a
commercial risk adjustment model on the task of prospective risk score
prediction.
