Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations - Dinov, Ivo D.; Heavner, Ben; Tang, Ming; Glusman, Gustavo; Chard, Kyle; Darcy, Mike; Madduri, Ravi; Pa, Judy; Spino, Cathie; Kesselman, Carl; Foster, Ian; Deutsch, Eric W.; et al. (PloS one, 2016)
Keywords: Aged; Algorithms; Alzheimer's disease; Alzheimers disease; Amyotrophic lateral sclerosis; Analysis; Analytics; Artificial intelligence; Big Data; Biology; Biology and Life Sciences; Biomarkers; Cerebellum; Classification; Complexity; Computer and Information Sciences; Data analysis; Data management; Data processing; Databases, Factual; Datasets; Demographics; Diagnosis; Diagnostic systems; Disease control; Disease Progression; Female; Forecasting; Genetics; Health risks; Heterogeneity; Humans; Informatics; Information management; Information science; Laboratories; Learning algorithms; Logistic Models; Machine learning; Male; Mathematical models; Medical diagnosis; Medical imaging; Medicine and Health Sciences; Model accuracy; Movement disorders; Neural networks; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Neurology; NMR; Nuclear magnetic resonance; Nursing schools; Parkinson disease; Parkinson Disease - diagnosis; Parkinson Disease - genetics; Parkinson Disease - pathology; Parkinson's disease; Parkinsons disease; People and Places; Physical Sciences; Predictions; Principal components analysis; Proteins; Research and Analysis Methods; Science; Science & Technology - Other Topics; Statistics; Support Vector Machine; Support vector machines; Trauma