Variable interactions in risk factors for dementia
O'Donoghue, Jim and Roantree, Mark and McCarren, Andrew (2016) Variable interactions in risk factors for dementia. In: IEEE Tenth International Conference on Research Challenges in Information Science, 1 -3 Jun 2016, Grenoble. France.
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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 hyperparameters for prediction and learns variable interactions in a non-linear survival-analysis paradigm. Demonstrating effectiveness, we evaluate this approach using longitudinal data with a relatively small sample size.
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