Siemens Medical Solutions, USA Inc.; Siemens; Siemens Healthcare; Siemens Healthcare; University of Wisconsin at Madison; Siemens Medical Solutions USA, Inc.; Department of Medicine, Harvard University
Workshop: Predictive Models in Personalized Medicine
7:30am – 6:30pm Saturday, December 11, 2010
Hilton: Sutcliffe B
The purpose of this cross-discipline workshop is to bring together machine learning and healthcare researchers interested in problems and applications of predictive models in the field of personalized medicine. The goal of the workshop will be to bridge the gap between the theory of predictive models and the applications and needs of the healthcare community. There will be exchange of ideas, identification of important and challenging applications and discovery of possible synergies. Ideally this will spur discussion and collaboration between the two disciplines and result in collaborative grant submissions. The emphasis will be on the mathematical and engineering aspects of predictive models and how it relates to practical medical problems.
Although related in a broad sense, the workshop does not directly overlap with the fields of Bioinformatics and Biostatistics. Although, predictive modeling for healthcare has been explored by biostatisticians for several decades, this workshop focuses on substantially different needs and problems that are better addressed by modern machine learning technologies. For example, how should we organize clinical trials to validate the clinical utility of predictive models for personalized therapy selection? The traditional bio-statistical approach for running trials on a large cohort of homogeneous patients would not suffice for the new paradigm and new methods are needed. On the other hand bioinformatics typically deals with the analysis of genomic and proteomic data to answer questions of relevance to basic science. For example, identification of sequences in the genome corresponding to genes, identification of gene regulatory networks etc. This workshop does not focus on issues of basic science; rather, we focus on predictive models that combine all available patient data (including imaging, pathology, lab, genomics etc.) to impact point of care decision making.
More recently, as part of American Re-investment and Recovery Act (ARRA), the US government set aside significant amounts of grant funds for cross-disciplinary research in use of information technology in improving health outcomes, quality of care and selection of therapy.
The workshop program will consist of presentations by invited speakers from both machine learning and personalized medicine fields and by authors of extended abstracts submitted to the workshop. In addition, there will be a slot for a panel discussion to identify important problems, applications and synergies between the two scientific disciplines.