CALL FOR CHAPTER PROPOSALS
Proposal Submission Deadline: March 31, 2016
A handbook edited by
Behrouz H. Far, Department of Electrical and Computer Engineering, University of Calgary
Amir Albadvi, Department of Industrial Engineering, Trabiat Modares University
Bijan Raahemi, Telfer school of Management, University of Ottawa
Elham A.Z. Noughabi, Department of Electrical and Computer Engineering, University of Calgary
To be published by IGI Global in IGI Book Series: Advances in Healthcare Information Systems and Administration (AHISA)
The volume of healthcare data generated by various sources in structured and semi-structured formats is increasing at a phenomenal rate. With this growth, there is an obvious need to develop efficient tools, skills, and techniques transforming this data into useful and actionable knowledge. As the healthcare data is characterized by its complexity, high volume and high dimensionality, there is a need for an extensive use of data science as an ideal way to manage and analyze this data. Making the choice to fully embrace data science would bring huge benefits to the healthcare management and leverage the healthcare system in all aspects.
Data Science is an interdisciplinary field of science and technology extracting knowledge and insights from data in various forms. Data science employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition and learning, visualization, predictive analytics, uncertainty modeling, data warehousing, data compression, computer programming, artificial intelligence, and high performance computing.
Data science utilizes data preparation, statistics, predictive modeling, machine learning and other methods and tools to investigate problems in various domains of healthcare. The benefits from data science have already been proven in healthcare industry. As examples, data science can help healthcare insurers detect fraud, healthcare organizations make customer relationship management decisions, physicians evaluate the effectiveness of treatments for specific diseases, physicians identify and control the chronic diseases and patients receive better and more affordable healthcare services. Data science can be also helpful to manage the problems associated with hospital services, patient recovery, resource planning, facility utilization, logistics, vaccination and emergency response.
It is obvious that the healthcare industry can reap significant benefits from data science in a wide aspect. Data science is becoming attractive to healthcare practitioners and researchers as it has the potential to provide valuable insights for effective healthcare decision-making in future years.
This handbook gives examples of how data science can be used in healthcare. In this Handbook, we survey different techniques and tools of data science and discuss how they can be implemented and applied in the domain of healthcare and medical decision-making. Some related tools and software programming frameworks are also proposed with applications and case studies.
Objective of the Handbook
This Handbook aims to capture a comprehensive view of the applications of data science in healthcare and medical decision making, while summarizing the corresponding computational tools and software programming frameworks. The Handbook will help the medical students and practitioners to understand the benefits of data science and to get a deep enough understanding to
- Know when a particular method or tool would be useful and be able to identify managerial challenges and problems that require such an analysis,
- Use and implement the technique,
- Analyze the results and use the obtained knowledge
Despite the importance and value of data science to healthcare and medical decision-making, there is a lack of a comprehensive text in this area. Accordingly, this handbook provides a comprehensive framework and a practical road-map for medical students and practitioners on how to use data science methods and tools.
This handbook is designed for those who would like to apply data science to improve healthcare and medical decision-making. Specifically, this handbook focuses on developing skills to use helpful techniques for improvement in the domain of healthcare. With this style, the handbook is suitable as a text for a course on data science in healthcare and medical decision making at the graduate level (master’s or doctorate’s), particularly for medical or health systems students. In addition, this handbook should be a valuable reference in using data science for self-study and a learning tool for healthcare and medical practitioners and managers in public health, medical research universities, governmental agencies, and the pharmaceutical industry. Data science, Computer science, information technology, mathematics and statistics students and researchers, who want to make the jump to healthcare research, can be also the target audience of this handbook.
Contributors are welcome to submit chapters on the various techniques in the areas of Mathematics, Statistical Analysis, Data Mining, and Machine Learning or other topics relevant to data science discussing one or more of the following aspects:
- overview of the theoretical aspects of the techniques,
- case studies and applications of the techniques in different areas of healthcare,
- relevant tools and software programming frameworks
The recommended topics include, but are not limited to:
Mathematical Techniques, including
- Linear programming
- Multi-objective programming
- Queuing theory
- Simulation modeling
- Dynamic programing
- Network theory
- Complex network
Statistical Analysis Techniques, including :
- Discriminant analysis
- Principal component analysis
Data Mining and Machine Learning Techniques, including:
- Data mining (classification, clustering, association rules discovery)
- Text mining
- Image processing and mining
- Signal processing
- Machine learning
- Big data analytics
Other topics in Data Science, including:
- Data engineering
- Predictive analytics
- Data warehousing
- Other topics can also be considered as long as they are relevant to the handbook theme.
Researchers and practitioners are invited to submit a 2-3 page chapter proposal explaining the general contents of the chapter before March 31, 2016. Authors of accepted proposals will be notified by April 30, 2016. Full chapters are expected to be submitted by Jun 30, 2016. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for the Handbook.
All proposals and full chapters should be submitted through the E-Editorial DiscoveryTM online submission manager here: http://www.igi-global.com/publish/call-for-papers/call-details/2129
The Handbook is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the “Information Science Reference” (formerly Idea Group Reference), “Medical Information Science Reference,” “Business Science Reference,” and “Engineering Science Reference” imprints. For additional information regarding the publisher, please visit www.igi-global.com. This handbook is anticipated to be released in 2017.
March 31, 2016: Proposal Submission Deadline
April 30, 2016: Notification of Acceptance
Jun 30, 2016: Full Chapter Submission
Aug 31, 2016: Review Results Returned
October 31, 2016: Final Chapter Submission
January 30, 2017: Final Deadline
Elham A.Z. Noughabi
Department of Electrical and Computer Engineering, University of Calgary
2500 University Dr. NW, Calgary, Alberta, Canada
Tel.: +1 (403) 210 – 5479
E-mail: firstname.lastname@example.org; email@example.com