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:

Vishal Sharma

Partner:

OKAKI

Discipline:

Epidemiology / Public health and policy

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program:

Accelerate

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