Connectionists: VLDB Workshop on Data Management and Analytics for Medicine and Healthcare (DMAH 2017)

[Gang Luo]

                        -- Call for Papers --

    The Third International Workshop on Data Management and Analytics
                     for Medicine and Healthcare (DMAH 2017)
                    In Conjunction with VLDB 2017
                  Munich, Germany, September 1, 2017
                         http://dmah.info/


Healthcare enterprises are producing large amounts of data through 
electronic
medical records, medical imaging, health insurance claims, surveillance, 
and
others. Such data have high potential to transform current healthcare to
improve healthcare quality and prevent diseases, and advance biomedical
research. Medical Informatics is an interdisciplinary field that studies
and pursues the effective use of medical data, information, and knowledge
for scientific inquiry, problem solving and decision making, driven by 
efforts
to improve human health and well being.

The goal of the workshop is to bring people in the field cross-cutting
information management and medical informatics to discuss innovative data
management and analytics technologies highlighting end-to-end applications,
systems, and methods to address problems in healthcare, public health, and
everyday wellness, with clinical, physiological, imaging, behavioral,
environmental, and omic- data, and data from social media and the Web. It
will provide a unique opportunity for interaction between information
management researchers and biomedical researchers for the interdisciplinary
field.


This workshop welcomes papers that address fundamental research issues
for complex medical data environments, data management and analytical
methods, systems and applications.

Topics of interest include, but not limited to:

Big data integration for medical data;
Data cleansing for noisy and missing data;
Medical data and knowledge management and decision support;
Data management technologies for medical data;
Semantic Web and ontologies for clinical and biomedical applications;
Medical natural language processing and text mining;
Data mining and knowledge discovery from medical data;
Algorithms to speed up the analysis of big medical data;
Innovative visualization techniques for query and analysis of medical data;
Medical image mining;
Medical information retrieval;
Data privacy and security for healthcare data;
Hospital readmission analytics;
Medical fraud detection;
Social media and Web data analytics for public health;
Data analytics for pervasive computing for medical care.

DMAH 2017 accept two types of papers:
1) Regular research papers reporting original research results or 
significant
    case studies (18 pages).
2) Extended abstracts presenting novel research directions or challenging
    problems (4 pages).


Important Dates:

Individual Workshop Papers: May 7, 2017
Notification of Acceptance: June 18, 2017
Camera Ready: July 2, 2017
Workshop date: September 1, 2017


All submitted papers will be rigorously reviewed. All accepted papers will
be made available as a workshop proceedings to be published by Springer 
LNCS.

Workshop Chairs:
    Fusheng Wang, Stony Brook University, USA
    Gang Luo, University of Washington, USA
    Edmon Begoli, Oak Ridge National Laboratory, USA

Program Committee:
Jesús B. Alonso-Hernández, Universidad de Las Palmas de Gran Canaria
Thomas Brettin, Argonne National Laboratory
J. Blair Christian, Oak Ridge National Laboratory
Carlo Combi, University of Verona, Italy
Kerstin Denecke, Bern Univ. of Applied Sciences
Dejing Dou, University of Oregon
Alevtina Dubovitskaya, EPFL
Peter Elkin, University at Buffalo
Vijay Gadapally, Massachusetts Institute of Technology
Zhe He, Florida State University
Guoqian Jiang, Mayo Clinic
Jun Kong, Emory University
Tahsin Kurc, Stony Brook University
Ulf Leser, Humboldt University of Berlin
Yanhui Liang, Stony Brook University
Fernando Martin-Sanchez, Weill Cornell Medicine
Casey Overby, John Hopkins University
Wolfgang Mueller, Heidelberg Institute for Theoretical Studies
Hua Xu, University of Texas Health Science Center