Big Data for Healthcare and Biomedical Research

Biomedical Informatics (BMI) is a multidisciplinary field that is motivated by our desire to improve diagnosis, clinical care, and human health, through novel computational approaches to use (and learn from) biomedical and clinical data. We use our expertise in computer science and informatics by developing various enabling tools, technologies, and algorithms to solve specific biomedical and clinical applications. And in doing so help advance our understanding of disease and treatment, and also develop useful software and applications. Members of the department work in a variety of areas that range from machine learning, healthcare middleware that levrages cloud computing, clinical information systems, clinically oriented image analysis and biomedical knowledge modeling. The driving applications for the various ongoing projects include cancer research, organ transplant, HIV, medical imaging, radiation therapy, and clinical data analytics. All development work that is undertaken is free and open-source. In previous years, GSOC students have worked on diverse projects such as: geospatial systems for exploring microscopy environments that leveraged Hadoop; GPU accelerated pipelines for computational analysis of digitized biopsies; interactive visualization platforms for viewing massive images (>1GB); systems for data agnostic sharing of biomedical research datasets; Apache Drill based data integration platform; CNN based high throughput analysis of digitized biopsies; and information visualization of heterogeneous medical data. Many of these projects have been published in reputable journals and presented at major conferences. Some of the projects proved to be so successful, that they were adopted in major national/international biomedical research initiatives.

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Technologies

  • java
  • medical imaging
  • deep learning
  • hadoop
  • python

Topics

  • Science and Medicine
  • cloud
  • web platform and services
  • precision medicine
  • big data visualization
  • data fusion
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2016