Predicting obesity and smoking using medication data: A machine-learning approach.

Journal: Pharmacoepidemiology and drug safety
PMID: 34611961

Abstract

PURPOSE: Administrative health datasets are widely used in public health research but often lack information about common confounders. We aimed to develop and validate machine learning (ML)-based models using medication data from Australia's Pharmaceutical Benefits Scheme (PBS) database to predict obesity and smoking.

Authors

  • Sitwat Ali
    Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • Renhua Na
    Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • Mary Waterhouse
    Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • Susan J Jordan
    Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • Catherine M Olsen
    Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • David C Whiteman
    Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • Rachel E Neale
    Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.

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