CALL FOR PAPERS
KDD 2013 Workshop: Data Mining for Healthcare
Date: August 11, 2013
Location: Chicago, USA
Important dates:
May 5 Submission deadline
May 30 Notification date
June 15 Camera Ready Submission
August 11 Workshop date
Nitesh Chawla, University of Notre Dame
Balaji Krishnapuram, Siemens Medical Solutions
Mohit Kumar, Accenture
Mohak Shah, GE Global Research
Jimeng Sun, IBM T.J. Watson Research Center
Submission Details:
All submissions must be in PDF format. Papers should be no more than 6
pages in length. The submissions should be in the standard double-column
ACM Proceedings style. The associated formatting and style files are
available at: http://www.acm.org/sigs/publications/proceedings-templates
Authors should submit the papers, by May 05, 2013 via email to:
dmh2013.organizers@gmail.com and should have "DMH Submission" in the
subject line.
Background:
Healthcare systems around the world are struggling to keep up with patient
needs, and improve quality of care while reducing costs at the same time.
In virtually every country, the cost of healthcare is increasing more
rapidly than the willingness and, more ominously, the ability to pay for
it. Recent reports showed that issues such as medical and medication
errors, faulty patient-physician communication and poor care coordination
exist in many countries.
At the same time automation in both healthcare services, data storage and
record keeping are introducing new challenges. More and more data is being
captured around healthcare processes in the form of Electronic Health
Records (EHR), health insurance claims, medical imaging databases, disease
registries, spontaneous reporting sites, and clinical trials. As this data
gets collected, government regulations are requiring healthcare providers
to not only store it in an electronic format but also use it in meaningful
ways. For example, one of the intended outcomes of the healthcare reform
in USA was meaningful or secondary use of EHRs to improve patient care.
Using this data in an effective way to improve quality of care and reduce
costs requires innovation in data mining as well as academic, industry and
government partnerships.
On the system automation front, these challenges manifest themselves in
the form of guarantees around healthcare system performances. For
instance, providing quality of service (QoS) assurances for remote
healthcare systems have become crucial to guarantee robust and timely
delivery of life-critical data, e.g., in a wireless sensor networking
setting. Ascertaining reliability, data transmission quality and system
availability have become increasingly more important as attempts are made
to make remote healthcare systems as major enablers.
Previous KDD workshops in this area have focused on isolated
healthcare-related domains such as bioinformatics or medicine. This
workshop is aimed at taking an integrated view of the entire healthcare
system and bringing together researchers (from both academia and industry)
as well as practitioners from different organizations involved in
healthcare to talk about their different perspectives and to share their
latest problems and ideas. The organization team of the workshop, itself,
presents an eclectic mix from academia and industry. The healthcare world
today consists of 4 key groups (the 4 P's of Healthcare): patients
(patient enablement sites/institutions), providers (hospitals, labs, and
clinics), payers (health insurance companies or government agencies that
pay for services), and pharmaceuticals. This healthcare world can benefit
from engaging researchers and practitioners to not only help with the
design and management of EHRs but also with applying existing and
developing new data mining techniques for improved patient care, cost
reduction, and meeting the meaningful/secondary use of EHR. However, a
venue for these different parties to get-together is missing, and this
lack of interaction can be detrimental to all parties – healthcare world,
academic researchers and industry practitioners.
The goals of this workshop are:
The main goal of the workshop is to bring together researchers from
related areas (e.g., computer scientists, statisticians, epidemiologists,
and economists), practitioners, and healthcare professionals to assess the
state-of-the-art, share ideas and form collaborations. In summary, this
workshop will strive to emphasize the following aspects:
• Addressing the fundamental challenges in improving healthcare and how
data mining technologies will help
• Presenting recent advances in data mining algorithms and methods for
healthcare transformation
• Identifying the next step of healthcare solutions and the possible data
driven solutions
• Fostering interactions and collaborations among researchers and
practitioners (from different backgrounds), and healthcare professionals,
to promote cross-fertilization of ideas.
• Exploring unified platforms and data for better evaluation of the
techniques
• Deployed healthcare applications of data mining
• Developing and addressing challenges around healthcare systems and
automation
• New classes of research problems motivated by real-world business
problems data mining applications as components of healthcare business
processes
• How data mining is useful for various participants in the healthcare system
o Patients
o Providers (hospitals, labs, clinics)
o Payers (Insurance companies)
o Pharmaceuticals
Topic of Interest
Topic areas for the workshop include (but are not limited to) the following:
• Statistical analysis and characterization of healthcare data
• Meaningful use of healthcare data for improved patient care and
cost-reduction
• Data quality assessment and improvement: preprocessing, cleaning,
missing data treatment
• Pattern detection and hypothesis generation from observational data
• Comparative effectiveness research
• Medical information retrieval
• Cloud-computing models and scalability
• Healthcare systems
• Privacy and security issues in healthcare
• Information visualization for healthcare data
• Information fusion and knowledge transfer in healthcare
• Evolutionary and longitudinal patient and disease models
• Mining knowledge from medical imaging data
• Medical fraud detection
• Case based reasoning
• Clinical decision support
• Bio-surveillance
• Informed consent
• Intelligent payment models
• Collaborative care delivery models
• Post-market surveillance of medical interventions
• Text mining - mining free text in electronic medical records
• Improving Clinical trial process
• Pay for performance models in healthcare
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