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Bioprofiling over grid for personalised eHealthcare for AD

Emmanuel Ifeachor, Zervakis Michalis, Pin Hu, Lingfen Sun, Nigel Hudson

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Περίληψη

This paper is concerned with the exploitation of new information and communications technologies (computational intelligence algorithms and tools for biodata analysis, grid computing and web services, clinical user interfaces) to create a knowledge infrastructure to support personalised care for Alzheimer's disease (AD)-prevention, early detection, diagnosis, monitoring of progression and response to treatment. This revolves around the use of bioprofiles which is a personal 'fingerprint' that fuses together a person's current and past bio records and lifestyle. Analysis of an individual's bioprofile makes it possible to personalise care for AD. The paper is based on the work undertaken within the EU-funded project, BIOPATTERN. It provides highlights and insights into the requirements and challenges of personalised care for AD, the characteristic features and requirements of bioprofiles within the context of AD, techniques for the acquisition of useful parameters for inclusion in the bioprofile for AD, and a grid-based prototype system to demonstrate the concepts of bioprofiling for AD within the EU setting.

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