Developing a visual analytics system for predicting AKI and identifying its risk factors among hospitalized patients

Acute kidney injury (AKI) is defined as a sudden loss of kidney function over a short period of time. It is one of the most common clinical events in hospitalized patients. AKI can lead to a lower chance of survival and prolonged hospital stays. Therefore, early detection and diagnosis of AKI allows for simple management plans to be used to lower the length and severity of AKI and its associated mortality. The main goal of this project is to identify the patients who are at high risk of developing AKI by analyzing patient medical history data using advanced computational techniques. AKI is associated with estimated health care costs of more than $200 million in Canada annually. Therefore, successful implementation of this project will result in a substantive healthcare cost savings.

Neda Rostamzadeh
Faculty Supervisor: 
Kamran Sedig
Partner University: