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Big Data Analytics & Health Informatics

A number of different data streams (e.g. imaging, pathology, genomics, electrophysiology) are routinely acquired in the clinic for disease characterization. However most of this acquired “Big Data”, which contain cues on disease behavior and patient outcome, remains largely under-exploited and un-interrogated. The paucity of analytic and biomedical informatics tools to collectively harness and hence “unlock” quantitative, disease related insights from Big biomedical data, has often led to calls for better, higher resolution technologies or additional tests. However, much value and knowledge remains to be gained from routinely acquired clinical Big Data, including deeper insights into disease processes and mechanisms. This is especially true at a time of spiraling health care costs, where the need of the hour is “faster, cheaper, and better” and to maximize mileage from “standard of care” data.

Health informatics is a rapidly emerging that applies computer and information science to health sciences research, education, and patient care.

Biomedical informatics is a cross-cutting, interdisciplinary field that identifies, explores, and implements effective uses of data, information, and knowledge to improve the decision-making and problem-solving efforts to improve human health. The discipline of Big Data analytics and health Informatics is a rapidly growing strategic focus within the department of Biomedical Engineering at CWRU. Faculty and students are involved in developing and applying a variety of Big Data analytic tools to imaging, digital pathology, genomics, proteomics, and electrophysiological data with the goal of assisting physicians solve clinical translational problems.

 

Faculty

A. Bolu Ajiboye, Ph.D.

Development and control of brain-computer-interface (BCI) technologies for restoring function to individuals who have experienced severely debilitating injuries to the nervous system, such as spinal cord injury and stroke

 

Colin K. Drummond, Ph.D.

Professor and Assistant Chair; medical device design, microfabrication packaging, sensor systems, and cross-platform software systems integration.

 

Anant Madabhushi, Ph.D.

Quantitative image analysis; Multi-modal, multi-scale correlation of massive data sets for disease diagnostics, prognostics, theragnostics: cancer applications.

 

Cameron McIntyre, Ph.D.

Chronic high-frequency electrical stimulation of subcortical brain structures-- or deep brain stimulation (DBS)-- and its applications as a therapy for neurological disorders.

 

Gerald Saidel, Ph.D.

Mass and heat transport and metabolism in cells, tissues, and organ systems; mathematical modeling and simulation of dynamic and spatially distributed systems; optimal nonlinear parameter estimation and design of experiments

 

Nicole Seiberlich, Ph.D.

Advanced signal processing and data acquisition techniques for real-time MRI

 

Pallavi Tiwari, Ph.D.

Computerized decision support methods for evaluating disease presence and treatment response of radiotherapy and laser ablation therapy for neurological applications: brain tumors, epilepsy, and cancer pain. Novel automated algorithms to analyze and integrate multi-modal imaging data for disease diagnosis, prognosis, and treatment evaluation.

Satish Viswanath, Ph.D.

Medical image analysis, image radiomics, and machine learning schemes, focused on the use of post-processing, co-registration, and biological quantitation of imaging data. Applications in image-guided interventions, predictive guidance, and quantitative treatment response characterization in gastrointestinal cancers and inflammatory diseases.

David Wilson, Ph.D.

Biomedical image processing; digital processing and quantitative image quality of X-ray fluoroscopy images; interventional MRI