Mining Administrative Data to Predict Falls in the Elderly Population

  • Arian Hosseinzadeh
  • Masoumeh Izadi
  • Doina Precup
  • David Buckeridge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)


Falls among the elderly are very common and have a great impact on the health services and the community, as well as on individuals. Many medical studies have focused on the possible risk factors associated with falling in the elderly population, but predicting who is at risk for falling is still an open research question. In this paper, we investigate the use of supervised learning methods for predicting falls in individuals based on the administrative data on their medication use. The data is obtained from a cohort of elderly people in the province of Quebec, and our preliminary empirical investigation yields promising results.


Medication Class Class Imbalance Sedate Drug Supervise Learning Method Receive Operator Curve Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arian Hosseinzadeh
    • 1
  • Masoumeh Izadi
    • 2
  • Doina Precup
    • 1
  • David Buckeridge
    • 2
  1. 1.School of Computer ScienceMcGill UniversityCanada
  2. 2.Health Informatics Research GroupMcGill UniversityCanada

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