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Deep learning for prediction of population health costs

Item Type:Preprint
Title:Deep learning for prediction of population health costs
Creators Name:Drewe-Boss, P. and Enders, D. and Walker, J. and Ohler, U.
Abstract: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.
Keywords:Deep Learning, Health Insurance Claim Records, Health Cost Prediction
Publisher:Cornell University
Article Number:2003.03466
Date:10 March 2020
Official Publication:https://arxiv.org/abs/2003.03466
Related to:
https://edoc.mdc-berlin.de/21234/Final version

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