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Risk adjustment has become an increasingly important tool in healthcare. Ithas been extensively applied to payment adjustment for health plans to reflectthe expected cost of providing coverage for members. Risk adjustment models aretypically estimated using linear regression, which does not fully exploit theinformation in claims data. Moreover, the development of such linear regressionmodels requires substantial domain expert knowledge and computational effortfor data preprocessing. In this paper, we propose a novel approach for riskadjustment that uses semantic embeddings to represent patient medicalhistories. Embeddings efficiently represent medical concepts learned fromdiagnostic, procedure, and prescription codes in patients' medical histories.This approach substantially reduces the need for feature engineering. Ourresults show that models using embeddings had better performance than acommercial risk adjustment model on the task of prospective risk scoreprediction.

Medical Concept Representation Learning from Claims Data and Application to Health Plan Payment Risk Adjustment