Data Mining for Healthcare Management (DMHM) has been instrumental in detecting
patterns of diagnosis, decisions and treatments in healthcare. Data mining has
aided in several aspects of healthcare management including disease diagnosis,
decision-making for treatments, medical fraud prevention and detection, fault
detection of medical devices, healthcare quality improvement strategies and
privacy. Data Mining for Healthcare Management (DMHM) is an emerging field where
researchers from both academia and industry have recognized the potential of its
impact on improved healthcare by discovering patterns and trends in large amounts
of complex data generated by healthcare transactions. Data mining also helps to
discover interesting business insights to help make business decisions that can
influence cost efficiency and yet maintain a high quality of care.
Healthcare management has received great deal of attention in recent times and
application of data mining techniques to this field is gaining increasing
popularity. This workshop will provide a common platform for discussion of
challenging issues and potential techniques in this emergence field of data
mining for health care management. It will also serve as a critical and
essential forum for integrating various research challenges in this domain and
promote collaboration among researchers from academia and industry to enhance the
state-of-art and help define a clear path for future research in this emerging
area. Data Mining for Healthcare Management (DMHM) workshop will facilitate
collaboration among different disciplines including medicine, clinical
studies, embedded systems, hardware and computer science.
DMHM 2011 encourages the following topics (but is not limited to) related
application of data mining techniques to healthcare:
Theoretical foundations in Data Mining
Data models for healthcare management
Medical decision making
Evidence based medicinal decisions
Medical Insurance Fraud Detection
Patient Flow Models in Hospitals
Clinical data analysis
Cloud-computing models and challenges for healthcare.
Privacy and security in healthcare.
Improving Quality of products and services
Data collection and integration techniques
Data cleaning and transformation
Knowledge based medical recommendation models
Information visualization of medical data.
Enhancing quality of tools available to healthcare providers.
Medical device fault detection and prevention.
Reliability of medical devices.
Pattern recognition in medical images and data.
All papers must be submitted electronically using
EasyChair Website for the workshop in PDF format
only. Submitting a
paper to the workshop means that if the paper is accepted, at least one
the workshop to present the paper. Attendees are required to register at
We are currently negotiating with Springer-Verlag to include some
papers in the LNCS/LNAI series. We will keep you posted on the same.
Workshop paper submission deadline: 10
Workshop Organization Chairs
Dr. Prasanna Desikan
Cardiac Device Telematics
Prof. Jaideep Srivastava
Computer Science & Engineering
University of Minnesota
Prof. Ee-Peng Lim
School of Information Systems
Singapore Management University
For any further questions please contact Prasanna at prasanna
[at] gmail [dot] com
The views and opinions expressed in this page are strictly those of the page author.
The contents of this page have not been reviewed or approved by the University of Minnesota.