Today with the proliferation of big data tools and technologies it is possible to manage and analyse any type of datasets to extract valuable and useful insights automatically and take data-driven decision making to new level.
The data component of big data can be broken down into two broad categories: human-generated and machine data. The human generated data is the information that is generated when humans directly interact with an online system or record information, such as a clinical trial data set, or patient records. Machine data is generated directly by machines (without direct intervention of humans). Sensor-based data (like data generated by Point-of-Care diagnostics devices) falls into the machine data category. Utilizing machine data for research purposes in an automatic way has been largely ignored until recently. However, machine data is a vast and as yet untapped potential as a source of highly valuable information, as they contain important insights about the system as well as users of the system.
Big data is of great significance to public health because of the potential in all fields of medical research including its use in everything from genetic research to advanced medical imaging and research on improving quality of care. If enough data is captured from multiple sources, this data can be applied practically and quickly at the right time to help save lives.
Objective and Scope
The main objective of the intended book is to write about issues, challenges, opportunities, and solutions in novel research projects about data science in healthcare. The topics of interest include, but are not limited to:
• Efficient storage, management and sharing large scale medical data
• Novel approaches for analysing medical data using big data technologies
• Implementation of high performance and/or scalable and/or real-time computation algorithms for analysing big data in healthcare
• Usage of various data sources like historical data, social networking media, machine data and crowd-sourcing data in healthcare
• Using machine learning, visual analytics, data mining, spatio-temporal data analysis and statistical inference in the healthcare (with large scale datasets)
• Legal and ethical issues and solutions for using, sharing and publishing large medical datasets and the results of data analytics, security and privacy issues
Submission and publication procedure
1- Interested authors need to submit a brief chapter proposal to pouria.amirian@ndm.ox.ac.uk (less than 2 pages) in *.doc, *.docx or *.pdf formats by January 19, 2015.
2- Notification regarding the acceptance or rejection of each chapter proposal will be sent by Monday January 26, 2015. At that time, authors whose chapter proposals have been accepted will also be e-mailed guidelines regarding full book chapter preparation.
3- The full book chapter deadline is Monday 23 March, 2015. Following receipt, full chapters will be sent out for double-blind review.
4- Result of review will be sent to authors on Monday 13 April, 2015.
5- The revised chapters will be sent by authors on Monday 4 May, 2015.
Important dates
A. January 19, 2015: Chapter proposal (less than 2 pages) submission deadline
B. January 26, 2015: Notification of chapter acceptance/rejection and invitation to submit full chapters
C. March 23, 2015: Final chapter submission deadline
D. April 13, 2015: Acceptance/rejection and comments notification sent to author(s)
E. May 4, 2015: Final revised chapter submission deadline
F. June or July, 2015: Expected publication of book
More Information
For more detailed information or questions about the book, please contact by email
Dr. Pouria Amirian: pouria.amirian@ndm.ox.ac.uk (The Global Health Network, Centre for Tropical Medicine and Global Health at the University of Oxford)
This project is kindly sponsored by The Global Health Network and Oxford University and will be published by Springer.