Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals.

Journal: PloS one
PMID: 37549164

Abstract

BACKGROUND: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables.

Authors

  • Alexandros Katsiferis
    Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Samir Bhatt
    Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.
  • Laust Hvas Mortensen
    Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Swapnil Mishra
    MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
  • Majken Karoline Jensen
    Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
  • Rudi G J Westendorp
    Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.

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