Topic ID: |
3007 | |
Title: |
Multi-instance Learning for Prediction of Days in Hospital | |
Supervisor: |
Alan Blair | |
Research Area: |
Machine Learning, Artificial Intelligence | |
| Associated Staff | ||
|---|---|---|
Assessor: |
Mike Bain | |
| Topic Details | ||
Status: |
Active | |
Type: |
R & D | |
Programs: |
CE BINF SE | |
Group Suitable: |
No | |
Industrial: |
Yes | |
Pre-requisites: |
COMP9417 or COMP3411 | |
Description: |
In days of hospital prediction, historical patient health claims data are used to determine how many days the patient will spend in hospital in the following year. As each patient may have several claims, the problem is best represented as a multi-instance learning problem. However, current multi-instance learning research commonly follow the standard multi-instance learning assumption. The standard assumption suggests that bag labels can be determined from a single within-bag instance, which is inappropriate for the particular problem domain. The thesis introduces a technique for multi-instance regression with relaxed assumptions, and demonstrate the feasibility and performance of using Gradient Boosting Machines to learn both instance level and bag level functions. |
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Comments: |
Would suit a student with good coding skills and a background in machine learning or artificial intelligence. | |
| Past Student Reports | ||
| Simon Jing-Bong KEUNG in s2, 2012 Machine Learning for Fraud Detection |
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Download report from the CSE Thesis Report Library NOTE: only current CSE students can login to view and select reports to download. | ||