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CIBD Healthcare 2014 : Big data analytic for healthcare – a Special Session in Computational Intelligence in Big Data


When Dec 9, 2014 - Dec 12, 2014
Where Orlanda
Submission Deadline Jul 15, 2014
Notification Due Sep 5, 2014
Final Version Due Oct 5, 2014
Categories    healthcare   big data   data analytics

Call For Papers

Workshop format: half a day (exact date and time to be confirmed)

It is becoming a global trend to work in the intersection between big data and analytics in healthcare. According to one estimate, 500 petabytes of digital healthcare data were generated in 2012 across the globe. By the year 2020, there will be 50 times increase. Simply having more data does not necessarily mean better healthcare outcome, unless the whole system from algorithms and software to storage and workflow can harness the data more efficiently, more effectively, and more timely. Rather than subjecting people to a battery of tests, or following a prescribed guidelines, the future of medicine will move to evidenced-based medicine. Treatment will be more personalized, forward looking (predictive) and preventive, proactive, and require less guess work.

This special session will focus on the latest research on the topics of healthcare modelling with a particular emphasis on big data technology that render the technological solution to be more accurate, more robust, and more efficient.

List of topics and the scope

We solicit papers that deal with the following aspects as illustrative examples and not necessarily exhaustive:

* Privacy preserving big data analytics for healthcare
* Personalized healthcare modelling
* Dealing with diverse, longitudinal healthcare data
* Data mining and machine learning for healthcare
* Ontology enhanced healthcare analytics
* Patient record clustering
* Translational healthcare
* Genomic and gene wide studies

The topic of big data fits SSCI and within this, the healthcare area has a great untapped potential due to the volume, variety, veracity, and velocity of data. More efficient, and more intelligent algorithms are certainly needed to better exploit the wealth of untapped information locked in patient records.

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