Using administrative data to predict health outcomes: a machine learning approach

Adverse events due to benzodiazepine use and falls are important clinical outcomes in older adults. As well, high-cost users of the health care system, although small in number, are a substantial burden with regards to health costs. Continued use of BZRAs in older adults is concerning from a public health standpoint; 1 in 3 older adults experience a fall in the community; 5% of health care users consume about 60% of hospital and home care spending. Being able to identify these individuals would add to the current efforts to reduce health care burden. Currently, there are no risk assessment tools using administrative data to predict risk of these outcomes. Machine Learning offers a framework to use administrative data to identify high risk individuals and provide targets for intervention.

Faculty Supervisor:

Dean Eurich

Student:

Partner:

OKAKI

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

University:

University of Alberta

Program: