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User-Centric Healthcare Data Analysis 2015 : The Scientific World Journal Special Issue: User-Centric Healthcare Data Analysis


When N/A
Where N/A
Submission Deadline Jan 23, 2015
Categories    biomedical/health informatics   data mining   knowledge discovery   artificial intelligence

Call For Papers

Manuscript Due: Friday, 23 January 2015
First Round of Reviews: Friday, 17 April 2015
Publication Date: Friday, 12 June 2015

Big data is rapidly becoming the key issue of the modern connected world. Large volumes of storage and increasing computing power, coupled with the wide variety of data sources, mean that big data is being assembled in every area of life. The biomedical domain is a key example of this: technologies such as next generation sequencing, robust new medical devices, personal electronic health records, mobile applications, and social networks with population health data will need to be coherently harnessed in order to maximise benefit.

The future of healthcare is envisioned as a system that empowers people to interact more proactively with clinicians to lead to better treatment outcomes. Multidimensional challenges, however, are presented within this new healthcare scenario. They involve appropriate data management, data acquisition, fusion, integration, update, and security to the final analysis.

A further key emerging idea is the realisation that the results of big data analytics are themselves complex and multifaceted, including issues of knowledge representation, uncertainty, and interpretability. It is no longer sufficient to ‘simply’ run an algorithm on data and present the answer ‘as is’ to the user. A new suite of techniques and tools is needed in which users can explore the results of analysis; and different users may have different exploratory needs. In short, user-centric data analysis is needed, combining aspects of visualisation, presentation, and interactivity, to communicate the analysis results in an appropriate manner.

The purpose of this special issue is to bring together novel approaches for healthcare which addresses some of these issues from raw data. Potential topics include, but are not limited to:

Acquisition and interfacing for medical data
Decision support systems for professionals and patients
Patient guidance services, e.g., predictive algorithms of patient health status
Personalised medicine in healthcare systems
Digital representation and visualisation of health data to improve diagnosis and treatment
Biomedical and bioinformatics data integration and analysis
Knowledge extraction from image data sets
Emerging knowledge discovery of genetic causes of diseases

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