| PAKDD-2011 Tutorial: Datamining for Healthcare Management |
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Held in
conjunction with
The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining 24-27 May 2011 (Tue-Fri) - Shenzen, China |
| ABSTRACT |
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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. Data mining was initially a success in the
healthcare industry as it was used to detect fraudulent claims processing.
However, since then large collections of transactional data and also data
due to mergers and acquisitions has provided businesses enough
opportunities to analyze and extract informative hidden patterns to reduce
costs. Researchers from both academia and industry have recognized the
potential of data mining and its impact on improved healthcare by
discovering patterns and trends in large amounts of complex data generated
by healthcare transactions. Data mining essentially helps discover
interesting business insights to help
This tutorial will provide an up-to-date introduction to the increasingly important field of data mining in healthcare management, as well as an overview of research directions in this field. It will cover the most representative research activities and directions in data mining based healthcare management techniques. In this tutorial, we first provide an introduction to healthcare management and then survey the research in this field. Next, an overview of emerging research in data mining for healthcare is presented. This tutorial will help researchers by providing a survey on the research till date, enable the understanding of how data mining can be useful for healthcare management and motivate them to pursue new research in this field. It will also be useful for practitioners from industrial organizations to understand how data mining techniques can help them leverage the potential of large amounts of healthcare data that has been collected. |
| SHORT BIOGRAPHIES |
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Prasanna Desikan
Dr. Prasanna Desikan is currently Senior Scientific Advisor at Center for Healthcare Innovation, Allina Hospitals and Clinics. Previously he worked in Boston Scientific on their advanced patient monitoring system. Prior to that, he worked as a Senior Researcher for Infobionics Inc, based in Eden Prairie, Minn., where he was actively involved in developing a new database management system based on novel data model called Cellular Data Model. His current research focus is on 'Data mining for healthcare management'. He is co-organizing a tutorial on Data Mining for Healthcare Management at SIAM International Conference on Data Mining Conference, 2011 and PAKDD 2011. He has successfully co-organized workshop on Data mining for healthcare management at PAKDD 2010 and is co-organizing the same workshop at PAKDD 2011. His earlier research work primarily focused on areas of Web mining and link analysis. Early on he has collaborated in tutorials for Web Mining and has published book chapters providing research overview in the area. He received his Ph.D and M.S in Computer Science from University of Minnesota, Twin Cities, USA. Jaideep Srivastava Dr. Jaideep Srivastava is a Professor of Computer Science & Engineering at the University of Minnesota. He has established and led a laboratory that conducts research in databases, multimedia systems, and data mining. Dr. Srivastava has an active collaboration with the technology industry, both for research and technology transfer, and is an often-invited participant in technical and technology strategy forums. The US federal government has solicited his opinion on computer science research as an expert witness. He has supervised 26 Ph.D. dissertations, 55 M.S. theses, and a number of post-doctoral researchers. In the process, he has authored or coauthored over 250 papers in refereed journals and conferences, and as invited book chapters. A series of prototype software systems have built in his laboratory to evaluate and validate our research results; and have acted as vehicles of technology transfer. This research has been supported by leading government agencies, including NSF, NASA, ARDA, IARPA, NIH, CDC, US Army, US Air Force, and MNDoT; and a number of leading industries, including IBM, United Technologies, Eaton, Honeywell, Cargill, and Huawei Telecom. Dr. Srivastava has significant experience in the industry, in both consulting and executive roles. Specifically he has lead a corporate data mining team at Amazon.com (www.amazon.com) and built a data analytics department at Yodlee (www.yodlee.com) from the ground up. More recently, he spent two years as the Chief Technology Officer for Persistent Systems (http://en.wikipedia.org/wiki/Persistent_Systems), where he built an R&D division with a number of Centers of Excellence people from the ground up. In addition, he oversaw the redesign of the training and technical vitalization program for 2,200+ engineers; and also established collaborative research programs with a number of academic institutions. He has provided technology and technology strategy advice to a number of large corporations including Cargill, United Technologies, IBM, Honeywell, KPMG, 3M, TCS, and Eaton. He has served in an advisory capacity to a number of small companies, including Lancet Software, Infobionics, and Contata. He has served as Technology Advisor to the State Government of Minnesota and to the Government of India. He has previously held, and currently holds distinguished visiting professorships at Heilongjiang University and Wuhan University, respectively. Dr. Srivastava has a Bachelors from the Indian Institute of Technology (IIT), Kanpur, India, and MS and PhD from the University of California, Berkeley. He has been elected as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the world.s premier professional society in the field. He has been appointed as IEEE.s Distinguished Visitor, and has given over 150 invited talks in over 30 countries, including over a dozen keynote addresses at major conferences. Kuo-Wei (David) Hsu Dr. Kuo-Wei Hsu is an Assistant Professor in the Department of Computer Science at the National Chengchi University, Taipei, Taiwan. His research interests include ensemble learning, database support for data mining, and data mining applications to tax and healthcare data sets. Before starting his doctoral study, he worked as an Information Engineer at the National Taiwan University Hospital, where he participated in the development of a Health Information System. He received his Ph.D. in Computer Science from University of Minnesota, Twin Cities, USA. |