AI-Based Predictive Modelling of the Onset and Progression of Dementia

Smart Cities

Elisabeth Stögmann¹  Helena Untersteiner¹  Tiia Ngandu²  Francesca Mangialasche³  Patrizia Mecocci⁴  Markus Bödenler⁵  Bernhard Neumayer⁵  Sten Hanke⁵ 

[1] Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria;Department of Public Health Solutions, Public Health Promotion Unit, Finnish Institute for Health and Welfare, 00271 Helsinki, Finland;Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 171 77 Stockholm, Sweden;Section of Gerontology and Geriatrics, Department of Medicine and Surgery, University of Perugia, 06123 Perugia, Italy;eHealth Institute, FH JOANNEUM University of Applied Sciences, 8020 Graz, Austria;

关键词: dementia;    ICT;    artificial intelligence;    machine learning;    intervention;    prevention;   

DOI  :  10.3390/smartcities5020036

来源: DOAJ

【 摘 要 】

Dementia, the most severe expression of cognitive impairment, is among the main causes of disability in older adults and currently affects over 55 million individuals. Dementia prevention is a global public health priority, and recent studies have shown that dementia risk can be reduced through non-pharmacological interventions targeting different lifestyle areas. The FINnish GERiatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) has shown a positive effect on cognition in older adults at risk of dementia through a 2-year multidomain intervention targeting lifestyle and vascular risk factors. The LETHE project builds on these findings and will provide a digital-enabled FINGER intervention model for delaying or preventing the onset of cognitive decline. An individualised ICT-based multidomain, preventive lifestyle intervention program will be implemented utilising behaviour and intervention data through passive and active data collection. Artificial intelligence and machine learning methods will be used for data-driven risk factor prediction models. An initial model based on large multinational datasets will be validated and integrated into an 18-month trial integrating digital biomarkers to further improve the model. Furthermore, the LETHE project will investigate the concept of federated learning to, on the one hand, protect the privacy of the health and behaviour data and, on the other hand, to provide the opportunity to enhance the data model easily by integrating additional clinical centres.

【 授权许可】

Unknown