Abstract
Background: Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in
memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of
dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly
in clinical decision-making.
Main body: Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely
and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment.
However, the dementia care pathway is currently suboptimal. We propose that through computational approaches,
understanding of dementia aetiology could be improved, and dementia assessments could be more standardised,
objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of
data-driven computational neurology approaches and the development of practical clinical decision support
systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany
such implementations.
Conclusion: The data-driven era for dementia research has arrived with the potential to transform the healthcare
system, creating a more efficient, transparent and personalised service for dementia.
Keywords: Dementia, Alzheimer’s disease, Dementia care pathway, Data science, Computational neurology,
Computational modelling, Computational neuroscience, Healthcare economics, Clinical decision support systems
memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of
dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly
in clinical decision-making.
Main body: Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely
and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment.
However, the dementia care pathway is currently suboptimal. We propose that through computational approaches,
understanding of dementia aetiology could be improved, and dementia assessments could be more standardised,
objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of
data-driven computational neurology approaches and the development of practical clinical decision support
systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany
such implementations.
Conclusion: The data-driven era for dementia research has arrived with the potential to transform the healthcare
system, creating a more efficient, transparent and personalised service for dementia.
Keywords: Dementia, Alzheimer’s disease, Dementia care pathway, Data science, Computational neurology,
Computational modelling, Computational neuroscience, Healthcare economics, Clinical decision support systems
| Original language | English |
|---|---|
| Article number | 398 |
| Number of pages | 10 |
| Journal | BMC medicine |
| Volume | 18 |
| DOIs | |
| Publication status | Published - 16 Dec 2020 |
Bibliographical note
FundingThis project was supported by the European Union’s INTERREG VA
Programme, managed by the Special EU Programmes Body (SEUPB; Centre
for Personalised Medicine, IVA 5036), with additional support by the
Northern Ireland Functional Brain Mapping Project Facility (1303/101154803)
and funded by Invest Northern Ireland and the University of Ulster (KW-L),
Alzheimer’s Research UK (ARUK) NI Pump Priming (KW-L, PLM, ST, AJ) and
Ulster University Research Challenge Fund (KW-L, PLM, ST, AJ). The views and
opinions expressed in this paper do not necessarily reflect those of the
European Commission or the Special EU Programmes Body (SEUPB).
Availability of data and materials
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