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Alzheimer's disease is a progressive disease, with subtle signs appearing several years before the first clinical symptoms. Identifying subjects who show these signs, and who are likely to develop the disease in the coming years, is a crucial point that could allow researchers to better study the disease mechanism, select patients for clinical trials and tailor patient care. In this talk I will present several data driven methods aiming at identifying at risk patients, operating in various settings and targeting different use cases.