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Structural and Temporal Relationship Modelling of Electronic Health Records for Medical Risk Prediction

posted on 26.05.2021, 07:08 by BHAGYA GAYATHRI HETTIGE
Analysis of historical electronic health record (EHR) data for predicting the medical risks of patients is an essential task in personalized healthcare. Each patient has a sequence of hospital visits, and each visit contains an unordered set of medical codes. We identify two major aspects of EHRs: (1) structural relationships between visits and codes (i.e., code-sharing behaviours of visits) represented with a graph data structure, and (2) temporal relationships in the visit sequences (i.e., disease progression) modelled by a point process. We propose novel deep learning models to capture both these relationship types for accurate and interpretable medical risk prediction.


Campus location


Principal supervisor

Wray Buntine

Additional supervisor 1

Yuan-Fang Li

Additional supervisor 2

Suong Le

Additional supervisor 3

Teresa Wang

Year of Award


Department, School or Centre

Clayton School of IT

Additional Institution or Organisation

Monash Institute of Medical Engineering (MIME)


Doctor of Philosophy

Degree Type