Machine learning helps to identify early signs of Alzheimer's

Researchers at the University of Southern California have discovered "hidden" indicators of Alzheimer's in medical data that could result in earlier diagnosis of the disease and better prognosis for patients. Using machine learning, USC researchers identified potential blood-based markers of Alzheimer's disease that could be detected with a routine blood test. "This type of analysis is a novel way of discovering patterns of data to identify key diagnostic markers of disease," said Paul Thompson, associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute and professor in USC's Keck School of Medicine. "In a very large database of health measures, it helped us discover predictive features of Alzheimer's disease that nobody suspected were there." Also See: MRI brain scans better ID people likely to develop Alzheimer's In their study, published in Frontiers in Aging Neuroscience, the USC research team analyzed medical data in the Alzheimer's Disease Neuroimaging Initiative database--collected from 829 older adults--to identify predictors of cognitive decline and brain atrophy during a one-year period.