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Variable Interactions for Risk Factors in Dementia

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Current estimates predict 1 in 3 people born today will develop dementia, suggesting a major impact on future population health. As such, research needs to connect specialist clinicians, data scientists and the general public. The In-MINDD project seeks to address this through the provision of a Profiler, a socio-technical information system connecting all three groups.The public interact, providing raw data; data scientists develop and refine prediction algorithms; and clinicians use in-built services to inform decisions. Common across these groups are Risk Factors, used for dementia-free survival prediction. Risk interactions could greatly inform prediction but determining these interactions is a problem underpinned by massive numbers of possible combinations. Our research employs a machine learning approach to automatically select best performing hyper-parameters for prediction and learns variable interactions in a non-linear survival-analysis paradigm. Demonstrating effective-ness, we evaluate this approach using longitudinal data with a relatively small sample size.
Conference Name: 
IEEE Tenth International Conference on Research Challenges in Information Science
Proceedings: 
Proceedings of IEEE Tenth International Conference on Research Challenges in Information Science
Digital Object Identifer (DOI): 
10.na
Publication Date: 
02/06/2016
Conference Location: 
France
Institution: 
Dublin City University (DCU)
Open access repository: 
Yes