Machine learning can fill significant gap in Canadian public health data

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Reviewed by Emily Henderson, B.Sc.Nov 19 2020
Machine learning can be used to fill a significant gap in Canadian public health data related to ethnicity and Aboriginal status, according to research published today in PLOS ONE by a University of Alberta research epidemiologist.

Kai On Wong, senior data scientist at the Real World Evidence unit of the Northern Alberta Clinical Trials and Research Centre (NACTRC), said ethnicity and Aboriginal status are recognized as key social determinants of health but are often not reported in large databases that track acute and chronic diseases such as asthma, influenza, cancer, cardiovascular diseases, diabetes, disability and mental illness.


If a database currently lacks ethnicity information, we will not be able to tell whether certain ethnic groups have higher rates of disease or worse clinical outcomes. This is a way to unlock that missing dimension from existing data sources, which may help us understand, monitor and address issues such as social inequities and racism in Canada.”

Kai On Wong, Senior Data Scientist, Real World Evidence Unit, Northern Alberta Clinical Trials and Research Centre, University of Alberta Faculty of Medicine & Dentistry


Wong created a machine learning framework to analyze the names and geographic locations of 4.8 million …

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