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CALL FOR CONTRIBUTIONS NIPS 2010 workshop on Predictive Models in Personalized Medicine Whistler, BC, Canada, December, 2010 http://sites.google.com/site/personalmedmodels/ Important Dates: ---------------- Deadline for submissions: October 22, 2010 *NEW* Notification of acceptance: November 1, 2010 Confirmed Invited Speakers: ---------------- Erwin Bottinger, MD Mount Sinai Medical Center C. David Page, PhD University of Wisconsin at Madison R. Bharat Rao, PhD Siemens Medical Solutions USA, Inc. Background: ---------------- Recently there has been a paradigm shift from evidence based medicine to personalized medicine. Earlier optimal therapy selection based on populations e.g. If a patient belonged to a homogenous category such as T2 stage, node negative, non-metastatic, non-small cell lung cancer, the best treatment was selected on clinical trials for the various medications on the same population. Historically, treatment is identical for all members of this patient cohort. While this approach was developed to utilize the statistical power of significantly large sample of a relatively homogeneous group of patients, it ignores the heterogeneity of the individuals within the cohort. This is slowly being replaced by personalized predictive models utilize all available information from each patient (exams, demographics, imaging, lab, genomic etc.) to identify optimal therapy in an individualized manner. This approach improves outcomes because it exploits more detailed patient information to reduce uncertainty in predicting patient outcomes as a function of treatment. This finds applications in preventive care, diagnosis, therapy selection and monitoring. For example, a) predicting patients at risk of developing hypertension and preventing manifestation ahead of time with appropriate intervention (medications, diet, lifestyle changes etc.); b) improving the early detection of cancer in asymptomatic patient; c) selecting the optimal chemotherapy/radiation dosage or other therapy parameters based on patient characteristics. Chemotherapy is expensive with terrible side effects and often only works for less than 50% of the patients treated with it. Identifying the right subset of patients that can benefit from it reduces the costs and improves efficacy of the treatment. d) predicting patient response to a given medication or/and treatment: Often the outcomes of therapy manifest too late e.g. outcomes of chemo-radiation therapy in patients with non-small cell lung cancer may take many months to manifest. By monitoring surrogate markers, one may be able to predict poor outcomes early on and modify the therapy plan. Also by predicting patient response and adequate dosage for a given medication , undesirable possible drugs adverse side effects can be avoided. A good example of this is the recent work from the International Warfarin Pharmacogenetics Consortium (see references) on estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data. Goal: ---------------- 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, 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? 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. Topics of Interest: ---------------- We would like to encourage submissions on any of (but not limited to) the following topics: ** Applications - Personalized Medicine (individualized or sub-population based) - Preventive Medicine - Therapy Selection - Precision Diagnostics (Disease Sub-typing, Precise Diagnosis) - Companion Diagnostics and Therapeutics - Patient Risk Assessment (for incidence of disease) - Integrated Diagnostics combining modalities like imaging, genomics and in-vitro diagnostics. ** Algorithms/Theory - Dealing with missing data (e.g. data not missing at random) - Inductive transfer for reducing sample sizes - Feature Selection - Classification - Survival Analysis - Data Challenges (Noise, other pre-processing) - Statistical Methods for validating personalized predictive models (e.g. Clinical Trials) Submission Instructions: ------------------------ We call for paper contribution of up to 8 pages to the workshop using NIPS style. Accepted papers will be either presented as a talk or poster (with poster spotlight). They will also be available in an online proceedings that will be made available prior to the workshop. Extended versions of some accepted papers will also be invited for inclusion in an edited book on the same topic as the workshop. Papers should be emailed to the organizers at personalmedmodels.nip...@gmail.com. Please indicate your preference for oral or poster presentation. Organizers ---------- Faisal Farooq, Siemens Medical Solutions USA, Inc. Balaji Krishnapuram, Siemens Medical Solutions USA, Inc. Romer Rosales, Siemens Medical Solutions USA, Inc. Glenn Fung, Siemens Medical Solutions USA, Inc. Shipeng Yu, Siemens Medical Solutions USA, Inc. Jude Shavlik, University of Wisconsin at Madison Raju Kucherlapati, Harvard Medical School
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