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Big Multi-Modal Data Mining for Depression Precision Medicine
Yanqing Zhang
Department of Computer Science
Georgia State University
P.O. Box 5060
Atlanta, GA 30302-5060
U.S.A.
President Obama announced the Precision Medicine Initiative - a novel research effort to
revolutionize how we improve health and treat disease in the 2015 State of the Union address.
National Institute of Mental Health of NIH states that about 6.7% of U.S. adults experience
major depressive disorder each year. About 6% of Chinese people have major depressive
disorder annually. However, traditional depression diagnosis methods usually use one-domain
features such as molecular biological features, (2) brain imaging features, or (3) physiological
features. To overcome the conventional technical limitations, it is critical to have global multimodal data for further intelligent big data mining for depression precision medicine. Thus, it is
urgent to develop effective and efficient multi-modal data mining methods using parallel and
cloud computing to discover key multidisciplinary features from multidisciplinary depression
data to make accurate early depression diagnosis with multidisciplinary feature fusion. It is
necessary to develop new big data mining methods that can analyze multi-modal data sets
including (1) global depression data sets (brain data, genetic data and human behavior data), (2)
English biomedical literature in PubMed, and (3) Chinese biomedical literature to discover key
multidisciplinary features for accurate early depression diagnosis.