Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

Journal: PloS one
Published Date: Jan 1, 2020

Abstract

OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods.

Authors

  • Alexander Engels
    Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical-Centre Hamburg-Eppendorf, Hamburg, Germany.
  • Katrin C Reber
    Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical-Centre Hamburg-Eppendorf, Hamburg, Germany.
  • Ivonne Lindlbauer
    Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical-Centre Hamburg-Eppendorf, Hamburg, Germany.
  • Kilian Rapp
    Department of Clinical Gerontology and Rehabilitation, Robert-Bosch-Hospital, Stuttgart, Germany.
  • Gisela Büchele
    Department of Epidemiology and Medical Biometry, University of Ulm, Germany.
  • Jochen Klenk
  • Andreas Meid
    Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Heidelberg, Germany.
  • Clemens Becker
  • Hans-Helmut König
    Department of Health Economics and Health Services Research, Hamburg Center for Health Economics, University Medical-Centre Hamburg-Eppendorf, Hamburg, Germany.

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