Abstract
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.
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Hosseinzadeh, A., Izadi, M., Precup, D., Buckeridge, D. (2012). Mining Administrative Data to Predict Falls in the Elderly Population. In: Kosseim, L., Inkpen, D. (eds) Advances in Artificial Intelligence. Canadian AI 2012. Lecture Notes in Computer Science(), vol 7310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30353-1_28
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DOI: https://doi.org/10.1007/978-3-642-30353-1_28
Publisher Name: Springer, Berlin, Heidelberg
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