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From Statistical Genetics to Predictive Models in Personalized Medicine

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.

Workshop homepage: http://agbs.kyb.tuebingen.mpg.de/wikis/bg/NIPSPM11

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Reviews and comments:

Comment1 acceleratedrecovery, July 10, 2020 at 8:47 a.m.:

This is a very informative post because you have rise the basic needs points. There are few health problems in every country. Soon I will solve it.


Comment2 Rehab, July 10, 2020 at 8:48 a.m.:

This is a very informative post because you have rise the basic needs points. There are few health problems in every country. Soon I will solve it.
<a href="https://www.acceleratedrecovery.com/">Accelerated Recovery</a>

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