Achieving Clinical Automation in Paediatric Emergency Medicine with Machine Learning Medical Directives

Patients in the US and Canada have been suffering from overcrowding and long wait times in emergency departments, along with poor health conditions. In order to provide guidelines for nurses, medical directives can request certain diagnostic tests during triage, which speeds up the process by providing test results to the physician when making the initial assessment. Our research will use the data from patients’ electronic health records to build and validate various machine learning models and to predict the downstream testing needs for children. The organization can benefit from this research by applying the result to the pediatric emergency department, in a way that efficiently delivers health care to patients.

Intern: 
Xinqi Shen
Faculty Supervisor: 
Yun William Yu
Province: 
Ontario
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