File(s) under permanent embargo

Reason: Restricted by author. A copy can be supplied under Section 51(2) of the Australian Copyright Act 1968 by submitting a document delivery request through your library, or by emailing document.delivery@monash.edu

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

thesis
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.

History

Campus location

Australia

Principal supervisor

Wray Buntine

Additional supervisor 1

Yuan-Fang Li

Additional supervisor 2

Suong Le

Additional supervisor 3

Teresa Wang

Year of Award

2021

Department, School or Centre

Clayton School of IT

Additional Institution or Organisation

Monash Institute of Medical Engineering (MIME)

Course

Doctor of Philosophy

Degree Type

DOCTORATE