Deep learning for prediction of population health costs.

Journal: BMC medical informatics and decision making
PMID: 35114978

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.

Authors

  • Philipp Drewe-Boss
    Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Robert-Rössle-Strasse 10, 13125, Berlin, Germany. philipp.boss@posteo.de.
  • Dirk Enders
    Institute for Applied Health Research (InGef), Spittelmarkt 12, 10117, Berlin, Germany.
  • Jochen Walker
    Institute for Applied Health Research (InGef), Spittelmarkt 12, 10117, Berlin, Germany.
  • Uwe Ohler
    Department of Biology, Humboldt Universität zu Berlin, 10117 Berlin, Germany; The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 10115 Berlin, Germany; Department of Computer Science, Humboldt Universität zu Berlin, 10117 Berlin, Germany. Electronic address: uwe.ohler@mdc-berlin.de.

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