Machine learning approaches for integrating clinical and imaging features in late‐life depression classification and response prediction
International Journal of Geriatric Psychiatry2015Vol. 30(10), pp. 1056–1067
Citations Over TimeTop 10% of 2015 papers
Meenal J. Patel, Carmen Andreescu, Julie C. Price, Kathryn Edelman, Charles F. Reynolds, Howard Aizenstein
Abstract
Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment.
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