Pattern-Cog aims to improve dementia prevention strategies by developing and validating a machine learning-based personalised medicine framework for detecting the earliest signs of impending cognitive decline, enabling early and personalised multi-domain interventions. Findings from multi-domain lifestyle trials have emphasized that intervention effectiveness may be dependent on a methodology that does not yet exist, i.e., the accurate identification of at-risk individuals who are most likely to benefit. Pattern-Cog will address this methodological gap by (1) developing methods for predicting future cognitive decline based on clinical data and distinguishing between healthy individuals at higher risk for mild cognitive impairment and dementia and those who remain healthy; and (2) testing the methodology in ongoing dementia prevention trials. Instead of a standard machine learning approach, we propose an innovative concept of personalised aging pattern rooted in data from healthy individuals.
