IEEE Workshop on BigData in Bioinformatics and Health Care Informatics

IEEE International Workshop on BigData in Bioinformatics and Health Care Informatics

In a recent McKinsey & Co. study, it is claimed that the healthcare industry in U.S. alone could potentially save $450 billion a year with the help of advanced analytics, but healthcare organizations continue to struggle with managing and leveraging the vast stores of data they are building up. By 2011, U.S. healthcare organizations had generated 150 exabytes -- that's 150 billion gigabytes -- of data. Leading providers such as Kaiser Permanente alone might have as much as 44 petabytes of patient data just from its electronic health record (EHR) system, or 4,400 times the amount of information held at the Library of Congress. Add to this the insurance sector, the independent laboratories and individual health records, and the number is increasingly astounding both in terms of volume and in terms of variety of data sources.

There is a common theme emerging in the healthcare industry - big data enables unprecedented opportunity for aggregation and integration leading to cost effective and improved patient care. It is the aim of this workshop to bring together big data practitioners, researchers, students, clinicians, health IT experts, and data scientists to share ideas on how to improve the state of our healthcare systems by delivering on the promise of big data infrastructure investments.

We observe the same theme of data driven science in biological and biomedical research also. For example a next-generation sequencing experiment may easily generate terra-bytes of raw data. In biological imaging and biomedical imaging, large volumes of data are generated. How to store, achieve, index, manage, learn, mine, and visualize those data is clearly a challenge to the research community.

The first International Workshop on BigData in Bioinformatics and Health Care Informatics (BBH’13) offers a premier forum of presenting big data concepts, infrastructure, and analytics tools for integrating data from heterogeneous multimedia sources and expediting research in a wide range of areas including computer science, computational science, biological, biomedical, pharmaceutical, nursing, clinical care, dentistry, and public health.

Topics

Bioinformatics and Biomedical Informatics
• Next generation sequencing data storage and analysis
• Large scale biological network construction and learning
• Population based bioinformatics
• Genome structural change detection
• Large-scale bio-image and medical-image analysis
• Big data in molecular simulation and protein structure prediction
• Big data in systems biology
• Big data in drug discovery, development, and post-market surveillance
• Big data in semantics and bio-text mining

Healthcare System
• Security and Privacy for clinical data in big data infrastructures
• Health IT implementations and demonstrations
• Case Studies for Hadoop based healthcare analytics
• Benchmarking of big data infrastructure in healthcare
• Real time aspects of healthcare data infrastructure
• Novel data analysis algorithms that enable easy, rapid knowledge discovery from complex EMR
• Analytics for Visualizing and summarizing large patient data in EMR
• Novel algorithms and applications dealing with noisy, incomplete but large amounts of EMR data
• Integrating genomic data for improving human health
• Data science and modeling for health analytics
• Advances in new storage models for data variety (records, images, MRI, scans) for hospitals
• Big data challenges in Accountable Care settings
• Extracting meaning from multi-structured big data in realtime to improve outcome
• Combining information from Imaging (RIS, PACS), EHR, Labs, Genomics to give coherent diagnosis and treatment
• Leveraging social networks for data aggregation
• Smart visualizations for big data streams
• Big data and analytics from home monitoring devices
• Big data design patterns and anti-patterns

Health Data Analysis
• How to co-register patient data acquired over several time-points in their life?
• What are the important metadata that need to be tracked over the longitudinal duration?
• What software platforms need to be developed for enabling easy access to the patient’s medical and clinical history?
• How to handle gaps in history-taking?
• What is the current state-of-the-art in clinical decision support utilizing personalized longitudinal medical data and what is missing?

Important Dates
• Submission: July 26, 2013
• Notification of Acceptance: August 23, 2013
• Camera-ready: September 13, 2013
• Workshop: October 6-9, 2013

Paper Submission Site
https://wi-lab.com/cyberchair/2013/bigdata13/scripts/ws_submit.php, Following the link to the workshop 14 Big Data in Bioinformatics and Health Informatics.
Preparation of Submissions. Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines with length no more than 9 pages. See the submission website for formatting instructions. Though different formats may be accepted, PDF submission is encouraged.
All papers will be published at the workshop proceedings and at the IEEE digital library.

Workshop Website:
http://www.ittc.ku.edu/~jhuan/BBH/

Program co-Chairs:

• Jun (Luke) Huan, the University of Kansas
• Vinay Pai, NIH
• Ankur Teredesai, University of Washington, Tacoma
• Shipeng Yu, Siemens

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