A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia

Timothy Patterson, Sally McClean, Patrick Langdon, Shuai Zhang, CD Nugent, Ian Cleland

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

As the demographics of many countries shift towards an ageing population it is predicted that the prevalence of diseases affecting cognitive capabilities will continually increase. One approach to enabling individuals with cognitive decline to remain in their own homes is through the use of cognitive prosthetics such as reminding technology. However, the benefit of such technologies is intuitively predicated upon their successful adoption and subsequent use. Within this paper we present a knowledge-based feature set which may be utilized to predict technology adoption amongst Persons with Dementia (PwD). The chosen feature set is readily obtainable during a clinical visit, is based upon real data and grounded in established research. We present results demonstrating 86% accuracy in successfully predicting adopters/non-adopters amongst PwD.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages5928-5931
Number of pages4
DOIs
Publication statusPublished - 30 Aug 2014
Event36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Sheraton Chicago Hotel and Towers, Chicago, Illinois, USA
Duration: 30 Aug 2014 → …

Conference

Conference36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Period30/08/14 → …

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Prosthetics
Aging of materials

Cite this

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title = "A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia",
abstract = "As the demographics of many countries shift towards an ageing population it is predicted that the prevalence of diseases affecting cognitive capabilities will continually increase. One approach to enabling individuals with cognitive decline to remain in their own homes is through the use of cognitive prosthetics such as reminding technology. However, the benefit of such technologies is intuitively predicated upon their successful adoption and subsequent use. Within this paper we present a knowledge-based feature set which may be utilized to predict technology adoption amongst Persons with Dementia (PwD). The chosen feature set is readily obtainable during a clinical visit, is based upon real data and grounded in established research. We present results demonstrating 86{\%} accuracy in successfully predicting adopters/non-adopters amongst PwD.",
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Patterson, T, McClean, S, Langdon, P, Zhang, S, Nugent, CD & Cleland, I 2014, A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia. in Unknown Host Publication. pp. 5928-5931, 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 30/08/14. https://doi.org/10.1109/EMBC.2014.6944978

A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia. / Patterson, Timothy; McClean, Sally; Langdon, Patrick; Zhang, Shuai; Nugent, CD; Cleland, Ian.

Unknown Host Publication. 2014. p. 5928-5931.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - A Knowledge-Driven Approach to Predicting Technology Adoption among Persons with Dementia

AU - Patterson, Timothy

AU - McClean, Sally

AU - Langdon, Patrick

AU - Zhang, Shuai

AU - Nugent, CD

AU - Cleland, Ian

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AB - As the demographics of many countries shift towards an ageing population it is predicted that the prevalence of diseases affecting cognitive capabilities will continually increase. One approach to enabling individuals with cognitive decline to remain in their own homes is through the use of cognitive prosthetics such as reminding technology. However, the benefit of such technologies is intuitively predicated upon their successful adoption and subsequent use. Within this paper we present a knowledge-based feature set which may be utilized to predict technology adoption amongst Persons with Dementia (PwD). The chosen feature set is readily obtainable during a clinical visit, is based upon real data and grounded in established research. We present results demonstrating 86% accuracy in successfully predicting adopters/non-adopters amongst PwD.

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