• KongFatt Wong-Lin, Paula L McClean, Niamh McCombe, Daman Kaur, Jose M Sanchez-Bornot, Paddy Gillespie, Stephen Todd, David P Finn, Alok Joshi, Joseph Kane, and Bernadette McGuinness.
• Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Londonderry, Northern Ireland, UK. k.wong-lin@ulster.ac.uk.
• Bmc Med. 2020 Dec 16; 18 (1): 398.

BackgroundDementia 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 BodyDespite 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.ConclusionThe 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.

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