An intelligent healthcare system with peer-to-peer learning and data assessment

Xie, R 2018, An intelligent healthcare system with peer-to-peer learning and data assessment, Masters by Research, Science, RMIT University.

Document type:	Thesis
Collection:	Theses

Attached Files
Name			Description	MIMEType		Size
Xie.pdf			Thesis		application/pdf	4.61MB

Title	An intelligent healthcare system with peer-to-peer learning and data assessment
Author(s)	Xie, R
Year	2018
Abstract	Modern e-healthcare systems are prevalent in many medical institutions to reduce physicians' workload and enhance diagnostic accuracy, which leverages affordable wearable devices and Machine-Learning (ML) techniques. The healthcare systems collect various vital biosignals (e.g., heart rate and blood pressure) from wearable devices of users (e.g., chronic patients living alone at home) and analyze these patients' data in real-time by different ML classifiers (e.g. Support Vector Machine (SVM) or Hidden Markov Model (HMM)). The automatic diagnosis effectively improves the physicians' performance in terms of diagnostic efficiency and accuracy. There are three challenges impacting these healthcare systems -- the increasing number of patients, new diseases and the changes of existing disease patterns, which are caused by population aging as well as the alteration of environment and lifestyle. This research is intended to explore a novel healthcare system with advanced ML solutions that can solve the challenges and exhibit high accuracy and efficiency.
Degree	Masters by Research
Institution	RMIT University
School, Department or Centre	Science
Subjects	Pattern Recognition and Data Mining
Keyword(s)	Collaborative extreme learning machine Vital bio-signals Smart healthcare peer-to-peer learning data priority

Access Statistics:	69 Abstract Views, 79 File Downloads - Detailed Statistics
Created:	Thu, 07 Feb 2019, 14:53:25 EST by Keely Chapman

Research Repository