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IMA Hosts Workshop on Innovative Statistics and Machine Learning in Precision Medicine

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The IMA is pleased to announce a workshop on Innovative Statistics and Machine Learning in Precision Medicine, taking place September 14-16, 2017. The event kicks off a year-long program that will bring together experts from different disciplines, including statistics, biostatistics, machine learning, biomedicine, and mathematics, to exchange new research ideas and train graduate students.

Precision medicine is an emerging practice of medicine that uses a patient’s specific characteristics to guide decisions made with regard to the prevention, diagnosis, and treatment of diseases. Stimulated by the advancements in fields such as genomics and medical imaging, the last decade has witnessed exciting and remarkable progress in personalized medicine, ranging from treating breast cancer to treating major depressive disorder.

The success of precision medicine depends on the development of accurate and reliable statistical and machine learning tools used for estimating the optimal treatment regime based on data collected from randomized experiments or observational studies. The challenges of statistical and machine learning analysis of precision medicine include heterogeneity, high-dimensionality, limited number of samples, the need to integrate multiple data types, and the complexity of underlying biochemical mechanisms.

A variety of topics will be covered by the program, such as: individualized prediction of long-term cognitive changes over time; developing personalized treatment strategies that adapt with time-dependent outcomes, including patients’ response to previous treatments and side effects; investigating how a person’s individual characteristics influence the outcome of multiple medical treatments; and how an individual, care team, and hospital network can select and implement the treatment decision that will maximize the overall healthcare outcome.

The workshop will summarize progress and discuss future work, and visitors will be invited to collaborate with local researchers at the University of Minnesota throughout the year.