Project Code: 4902
Faculty: Faculty of Engineering
Department: Engineering Science
Main Supervisor: Dr Cameron Walker
Application open date: 16 Jun 2014
Application deadline: 11 Mar 2015
Enrolment information: NZ Citizens, NZ Permanent Residents, International
The expectations on clinicians to provide the best care possible for their patients continues to grow, largely due to the recent explosive increase in, and availability of, health-related data, information and knowledge. However, from a pragmatic perspective, these expectations are becoming increasingly unreasonable because there is now so much information and knowledge available within each area of clinical specialty that no individual could possibly retain all that is necessary to guarantee delivery of the best possible care. And this situation will only continue, particularly as we move towards increased utilisation of genomics, proteomics, etc., all of which generate masses of valuable data and information.
The solution to this significant problem of empowering clinicians to utilise all the available information and knowledge to maximise the care of their patients is to develop intelligent clinical support systems that incorporate this information and knowledge in a clinically relevant way that clinicians can easily and intuitively incorporate into their care plans. The proposed reserach project involves developing methods that use artificial intelligence techniques to assist clinicians
in their decision making in situations where the current state (level of health) of the system (patient) is unknown. Such techniques have been shown to work well in gaming environments, especially when significant quantities of historic game data are available. As data collection becomes integrated across the health sector and new sources of ‘big data’, such as genomics, become easily available, the power and potential of these techniques will be significant. This project aims to develop a clinical tool capable of utilizing huge quantities of patient data
to aid intelligent diagnosis and significantly improve the outcomes in a patient’s recovery (both in
terms of personal well-being and institutional cost).
Knowledge of artificial intelligence and/or machine learning. Experience with programming, particularly Java.
1) Data Mining with WEKA
2) Identification of Potential AI Methods
3) Prototyping of AI Methods
4) Testing/Comparison of AI Methods
5) Implementation/Integration of AI Methods