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Predicting risk of unplanned hospital readmission within 30-days of discharge using machine learning approaches
Unplanned hospital readmissions are a preventable and costly outcome in the health care system. There are limited tools to estimate risk of readmission. The machine learning process offers an opportunity to develop a risk predictor to identify those at high risk of readmission upon discharge. OKAKI has an opportunity to diversify the commercial products it can offer to health care administrators.
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:
Accelerate
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