Holzinger, A., Röcker, C. & Ziefle, M. (eds.) 2015. Smart Health: Open Problems and Future Challenges, Cham: Springer International, doi:10.1007/978-3-319-16226-3, [Springer Link], [DBLP], [Amazon], [Google Books]
Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems.
The very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning. The papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.
p-Health Smart Health Springer Book Project “The Aachener Project” (finished)
The publication of the “Blue Series Book” is completed, see > SpringerLink and we cordially thank all authors.
We have received a number of technological-mathematical papers, consequently we produce a separate technologically oriented volume – applying the same structure (including Glossary, Open Problems, Future Challenges) in the Springer Lecture Notes in Computer Science LNCS subseries State of the Art Surveys (SOTA) – finished by summer 2014 >>> EasyChair
For a recent example please refer to Springer SOTA “Interactive Knowledge Discovery and Data Mining in Biomedical Informatics” LNCS 8401 > SpringerLink
NOTE: The papers shall follow the style shown in the templates below.
Please find the templates here:
- docx-template MS Word > 0v1_MSWord_Template_for_LNCS_SOTA1
- Template in pdf > 0v1_MSWord_Template_for_LNCS_SOTA
- tex-template LaTeX > SOTA-Springer
Health costs worldwide are rapidly increasing. Demographic structures are dramatically changing. Technological advances are tremendously increasing. The invariable need for quality remains. From Smart Health to the Smart Hospital.
Advances in Biomedical Informatics & Biomedical Engineering provide the foundations for modern patient-centered healthcare solutions, health care systems, technologies and techniques. The majority of computer-supported healthcare solutions of the last decades focused on the support of care-givers and medical professionals; this changed dramatically with the introduction of pervasive healthcare technologies and the enormous success of mobile computing: in particular smart phones, handhelds and touch-tablet computers. Future technologies using the power of grid computers and supercomputing – driven by examples including IBM Watson and Apple Siri – will enable that the concept of pHealth provides support for a more diverse end user group to enable individualized and personalized medicine and health care. However, all these advances produce enormous amounts of data and one of the grand challenges in our networked world are indeed the large, complex, and often weakly structured and high-dimensional data sets, and the massive amounts of unstructured information. This “Big Data” V4 challenge (Volume, Variety, Velocity, Veracity) is most evident in the biomedical domain: the trend towards precision P4 medicine (Predictive, Preventive, Participatory, Personalized) has resulted in an explosion in the amount of generated biomedical data sets.
A synergistic combination of methodologies and approaches of two areas offer ideal conditions towards solving these challenges towards new, efficient and user-centered algorithms and tools: Human-Computer Interaction (HCI) and Knowledge Discovery & Data Mining (KDD), with the goal of supporting human intelligence with machine learning to interactively discover new, previously unknown insights into the data. Our leitmotiv is in making sense of “Big Data” by machine learning with the “human-in-the-loop”. The initial idea can be read [here],<springerlink>.
Seminal Papers are welcome addressing all aspects of pHealth; best practices, methods, methodologies, solutions, future perspectives and visionary models of smart health – ranging from sophisticated smart sensor networks, wearable computing, cloud-based services for healthcare, grid-based solutions, innovative biomedical devices and smart mobile applications towards aspects of enhanced user experience, social computing for health service support, data fusion, data integration and open medical data etc.
NOTE: This volume shall bring exclusive benefits for the readers and shall be of archival value on the desks and benches of both scientists and industrial practictioners, and shall be useful for joint projects at national, European, and international level. For this purpose each chapter is required to follow a specific structure:
- Introduction (A very short and concise introduction and motivation on why and how this chapter is important and for whom)
- Glossary (used terms shall be defined at first, so that a common understanding is guaranteed)
- State-of-the-art (this is the main part and may be divided into traditional subchapters accordingly)
- Open Problems (this shall highlight potential known issues so that others can avoid to make errors in advance)
- Future Outlook (this shall outline future research avenues, hot topics and research challenges of further interest)
CURRENT Submission procedure and schedule (closed)
- Please send all requests and communication directly to a.holzinger AT hci4all.at
- Our international Advisory Borad together with the HCI-KDD expert network will ensure the highest possible quality – each paper will at least be reviewed by 3 independent experts.
- We target to have all papers camera ready by October, 30, 2014.
International Advisory Board:
Gregory D. ABOWD, School of Interactive Computing, Georgia Tech, Atlanta, USA <expertise>
Rosa ARRIAGA, School of Interactive Computing, Georgia Tech, Atlanta, USA
Juan Carlos AUGUSTO, Department of Computer Science, Middlesex University, UK <expertise>
Jakob BARDRAM, IT University of Copenhagen, Denmark <expertise>
Bert BONGERS, University of Technology Sydney, Australia
Pam BRIGGS, Northumbria University, UK
Matt-Mouley BOUAMRANE, University of Glasgow, UK <expertise>
Stefan CARMIEN, Tecnalia, Spain
John M. CARROLL, Center for Human-Computer Interaction, Pennsylvania State University, US <expertise>
Brian CAULFIELD, University College Dublin, Ireland <expertise>
Mary CZERWINSKI, Microsoft Research, USA <expertise>
Anind K. DEY, Human-Computer Interaction Institute, Carnegie Mellon University, USA <expertise>
Paul FERGUS, School of Computing and Mathematical Sciences, Liverpool John Moores University, UK <expertise>
Marie GUSTAFSSON FRIBERGER, Computer Science Department, Malmö University, SE <expertise>
Eduard GROELLER, Computer Graphics Group, Vienna University of Technology, AT <expertise>
Erik GRÖNVALL, Aarhus University, Denmark <expertise>
Gourab Sen GUPTA, Massey University, NZ
Jim HOLLAN, University of California San Diego, USA
Maddy JANSE, Philips Research, the Netherlands
Ray JONES, University of Plymouth, UK <expertise>
Henry KAUTZ, University of Rochester, USA <expertise>
Shin’ichi KONOMI, The University of Tokyo, Japan
Kristof VAN LAERHOVEN, Embedded Sensing Systems, Department of Computer Science TU Darmstadt, DE <expertise>
Christine LISETTI, Florida International University, USA
Lenka LHOTSKA, Czech Technical University, Czech Republic
Paul LUKOWICZ, DFKI and University of Kaiserslautern, Germany <expertise>
Anthony J MAEDER, University of Western Sydney, Australia <expertise>
Marilyn McGEE-LENNON, University of Glasgow, UK <expertise>
Oscar MAYORA, CREATE-NET, Italy
Kevin McGEE, National University of Singapore
Jochen MEYER, OFFIS, Germany
Alex MIHAILIDIS, University of Toronto, Canada
Enid MONTAGUE, University of Wisconsin-Madison, USA <expertise>
Maurice MULVENNA, University of Ulster, UK <expertise>
Venet OSMANI, CREATE-NET, Italy
Gregory O’HARE, University College Dublin, Ireland <expertise>
Ant OZOK, University of Baltimore County (UMBC), USA
Kristin PAETZOLD, Universität der Bundeswehr München, Germany
Richard PAK, Clemson University, USA <expertise>
Thomas PLOETZ, Georgia Tech, USA <expertise>
Mihail POPESCU, Health Management and Informatics Deptartment, University of Missouri, USA <expertise>
Bernhard PREIM, Department of Simulation and Graphics, University of Magdeburg, DE <expertise>
Yvonne ROGERS, UCL Interaction Centre, University College London, UK <expertise>
Timo ROPINSKI, Scientific Visualization Group, Linköping University, SE <expertise>
Aleksandra SARCEVIC, Drexel University, USA
Andreas SCHRADER, University of Luebeck, Germany <expertise>
Young Seok LEE, Motorola Mobility Inc., USA
Stuart SMITH, Neuroscience Research, Australia
Duncan STEVENSON, Australian National University, Australia
Norbert STREITZ, Smart Future Inititiative, Darmstadt, Germany
Monica TENTORI, Computer Science Department, Ensenada, Mexico <expertise>
Upkar VARSHNEY, Georgia State University, USA <expertise>
Stefan WAGNER, Aarhus University, Denmark
May D. WANG, Department of Biomedical Engineering, Georgia Tech and Emory University, USA <expertise>
Nadir WEIBEL, Department of Computer Science and Engineering, University of California San Diego, USA <expertise>
Lauren WILCOX, Department of Biomedical Informatics, Columbia Unviersity New York, USA <expertise>
Keiichi YASUMOTO, Nara Institute of Science and Technology, Japan
Some background Information:
Health systems worldwide are challenged by big and complex sets of heterogeneous, high-dimensional, complex data and increasing amounts of unstructured information. Due to the fact that biomedicine, health and the life sciences are turning into a data intensive science, machine learning can help to more evidence-based decision-making and support to realize the grand goals of personalized medicine [Holzinger, A. 2014. Trends in Interactive Knowledge Discovery for Personalized Medicine: Cognitive Science meets Machine Learning. IEEE Intelligent Informatics Bulletin, 15, (1), 6-14].
A grand goal of future medicine is in modelling the complexity of patients to tailor medical decisions, health practices and therapies to the individual patient. This trend towards personalized medicine produces unprecedented amounts of data, and even though the fact that human experts are excellent at pattern recognition in dimensions of smaller than three, the problem is that most biomedical data is in arbitrarily high dimensions – much higher than three. This makes manual analysis difficult, yet often practically impossible. Consequently, experts in daily biomedical routine are decreasingly capable of dealing with the complexity of such data. Moreover, they are – understandably – not interested in struggling around with the complexity of their data sets. Rather, the experts need insight into the data, so to gain knowledge in order to support their direct workflows and to find answers to their questions and hypotheses. Therefore, it is necessary to provide efficient, useable and useful computational methods, algorithms and tools to discover knowledge and to interactively make sense of such high-dimensional data.