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Download Big Multi-Modal Data Mining for Depression Precision Medicine
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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.
