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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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thesis
posted on 04.11.2021, 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.

History

Campus location

Australia

Principal supervisor

Vincent Cheng-siong Lee

Additional supervisor 1

Kenneth Tan

Year of Award

2021

Department, School or Centre

Clayton School of Information Technology

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Information Technology