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Early Detection of Sepsis in Neonatal and Adult Patients Using Machine Learning Approaches - Aiding Diagnosis for Clinicians

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posted on 2021-11-04, 22:29 authored by YIFEI HU
This thesis systematically reviewed the relevant literature, summarised the current research status in computer-aided sepsis detection, and proposed a unified sepsis detection framework. Within this framework, we explored multiple methods, including classic machine learning and deep learning models, in the task of early detection of sepsis in infants and adults, and achieved promising outcomes that are able to assist clinicians in their decision making during the diagnostic process.


Campus location


Principal supervisor

Vincent Cheng-siong Lee

Additional supervisor 1

Kenneth Tan

Year of Award


Department, School or Centre

Clayton School of Information Technology


Doctor of Philosophy

Degree Type



Faculty of Information Technology