  Accurate prediction of healthcare costs is important for optimally managing
health costs. However, methods leveraging the medical richness from data such
as health insurance claims or electronic health records are missing. Here, we
developed a deep neural network to predict future cost from health insurance
claims records. We applied the deep network and a ridge regression model to a
sample of 1.4 million German insurants to predict total one-year health care
costs. Both methods were compared to Morbi-RSA models with various performance
measures and were also used to predict patients with a change in costs and to
identify relevant codes for this prediction. We showed that the neural network
outperformed the ridge regression as well as all Morbi-RSA models for cost
prediction. Further, the neural network was superior to ridge regression in
predicting patients with cost change and identified more specific codes. In
summary, we showed that our deep neural network can leverage the full
complexity of the patient records and outperforms standard approaches. We
suggest that the better performance is due to the ability to incorporate
complex interactions in the model and that the model might also be used for
predicting other health phenotypes.
