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A Mobile-Based Screening System for Data Analyses of Early Dementia Traits Detection

  • Mobile & Wireless Health
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Abstract

Existing early detection methods that deal with the pre-diagnosis of dementia have been criticised as not being comprehensive as they do not measure certain cognitive functioning domains besides being inaccessible. A more realistic approach is to develop a comprehensive outcome that includes cognitive functioning of dementia, as this will offer a robust and unbiased outcome for an individual. In this research, a mobile screening application for dementia traits called DementiaTest is proposed, which adopts the gold standard assessment criteria of Diagnostic and Statistical Manual of Mental Disorders (DSM-V). DementiaTest is implemented and tested on the Android and IOS stores. More importantly, it collects data from cases and controls using an easy, interactive, and accessible platform. It provides patients and their family with quick pre-diagnostic reports using certain cognitive functioning indicators; these can be utilized by general practitioners (GPs) for referrals for further assessment in cases of positive outcomes. The data gathered using the new application can be analysed using Artificial Intelligence methods to evaluate the performance of the screening to pinpoint early signs of the dementia.

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Funding

This study has not been funded by particular entity.

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Authors and Affiliations

  1. Digital Technologies, Manukau Institute of Technology, Auckland, New Zealand

    Fadi Thabtah, Ella Mampusti, Raymund Herradura & jithin varghese

  2. Department of Psychology, Centre for Applied Psychological Research, School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK

    David Peebles

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  1. Fadi Thabtah
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  2. Ella Mampusti
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  4. Raymund Herradura
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  5. jithin varghese
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Correspondence to Fadi Thabtah.

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Thabtah, F., Mampusti, E., Peebles, D. et al. A Mobile-Based Screening System for Data Analyses of Early Dementia Traits Detection. J Med Syst 44, 24 (2020). https://doi.org/10.1007/s10916-019-1469-0

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