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Deep learning for prediction of population health costs. | LitMetric

Deep learning for prediction of population health costs.

BMC Med Inform Decis Mak

Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Robert-Rössle-Strasse 10, 13125, Berlin, Germany.

Published: February 2022

AI Article Synopsis

Article Abstract

Background: 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.

Methods: 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 existing 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.

Results: We showed that the neural network outperformed the ridge regression as well as all considered models for cost prediction. Further, the neural network was superior to ridge regression in predicting patients with cost change and identified more specific codes.

Conclusion: 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812208PMC
http://dx.doi.org/10.1186/s12911-021-01743-zDOI Listing

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