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CPCP Retreat 2016: Computational Phenotyping for Breast Cancer Risk Assessment Symposium Video

Recommendations for mammogram frequency vary widely. This causes confusion among primary care physicians and their patients. Dr. Beth Burnside describes her work with the phenotype models for breast cancer screening project in conjunction with the Low-dimensional Representations Lab at the CPCP to model to predict risks factors for individual patients. Dr. Burnside and her research group integrate genetic and imaging data to predict risk of breast cancer in patients. By applying machine learning methods to this data, the lab is working to create a clinical decision support tool to support evidence-based conversations between physicians and patients. Use of this type of tool in the clinic will assist physicians and their patients in making informed decisions about how often an individual patient should have a mammogram.

CPCP Retreat 2016: Multi-Armed Bandit Algorithms and Applications to Experiment Selection Symposium Video

Dr. Kwang-Sung Jun works in the Value to Information Lab at the CPCP. In this talk, he describes a new computational method that can be used by researchers to suggest which experiments would be best to perform next. Biology labs often have large numbers of experiments that they could perform, knowing which to perform first saves researchers time and money. This method, an adaptation of an algorithm originally designed to predict gambling wins, helps researchers with this experiment selection task. In addition to this task, Dr. Jun also describes the application of this new algorithm to the New Yorker cartoon caption selection task as part of their crowd sourcing algorithm.

CPCP Retreat 2016: Neuroimage-Based Phenotyping and the Problem of AD Symposium Video

Alzheimer's Disease is clinically characterized by the appearance of plaques and tangles in the brain, the phenotypic manifestation in patients is dementia. In order to prevent Alzheimer's disease it is necessary to develop a predictive computational framework that can differentiate the effects of normal aging on the brain from the preclinical Alzheimer's disease. Dr. Sterling Johnson describes the work of the Neuroimage-based Phenotyping Project at the CPCP toward this predictive goal. The results of this work will be applied to identify people who are candidates for early intervention trials addressing prevention of Alzheimer's Disease during the earliest stages of the disease. These early stages often occur more then 20 years before the onset of dementia.

CPCP Seminar: Transforming Your Research Through High Throughput Computing Seminar Video

Lauren Michael works as a research computing facilitator in the high throughput computing (HTC) center at the University of Wisconsin. In this first part of this talk, she defines HTC, describing what it does and how utilizing this powerful computing resource can be used to facilitate research. Knowing what HTC is and how it works helps researchers to determine what types of problems can be addressed by HTC. HTC is a type of parallel computing, allowing a problem to be broken up into smaller problems that can be solved simultaneously on multiple computers. The second part of this talk covers specific information about using HTCcondor, the HTC computing resource that we have here at the University of Wisconsin-Madison

Big Privacy Symposium: Introductory and Welcoming Remarks Symposium Video

Sponsored by the CPCP, the Big Privacy Symposium focuses on solving the privacy concerns that relate to Big Data problems. The solution to this problem lays at the intersection of policy and computational approaches. This symposium focuses on bringing these two fields together to address privacy concerns in Big Data.

Big Privacy Symposium: Big Data, Big Headaches: Cultivating Public Trust in an Age of Unconsented Access to Identifiable Data Symposium Video

Dr. Barbara Evans discusses the numerous challenges associated with the use of Big Data. Drawing from her background in computational modeling and her years of practicing law, Dr. Evans describes the legal challenges associated with privacy. People are more willing to share their data if they know it will be used for their good or the good of society. However, after data is released for research purposes, how do we guarantee that it will be used for the good of society? How should the issue be addressed if the data is used in a biased or otherwise nefarious manner? Dr. Evans also addressed the issue of de-identification of data. Research shows that people are more willing to share their data if they know that it will be de-identified. Unfortunately, in this age of increasingly ubiquitous collection of rich datasets, we are encountering increasing numbers of datasets where re-identification is possible due to the richness of the data. Based on these concerns, Dr. Evans makes some recommendations for future policies that protect the privacy of those who share their data while ensuring that society can continue to benefit from the computational mining of Big Data.

Big Privacy Symposium: Does Publishing a Predictive Model for Precision Medicine Put Patient Privacy at Risk? Symposium Video

In the context of Big Data, privacy is often thought of as an issue that is managed by de-identifying datasets. However, what are the privacy concerns associated with publishing predictive computational models that are the product of the analysis of confidential datasets. In this talk, Dr. Matt Fredrikson discusses how computational models can potentially be exploited to uncover information from confidential datasets. To prevent this from happening there are precautions that can be taken before publishing predictive models. However, research from Dr. Fredrikson's group shows that the current methods sacrifice either the utility of the model or the confidentially of the data used to create the model.

Big Privacy Symposium: Panel Discussion Symposium Video

Join our panel of experts as they apply their collective experience in Big Data Privacy to answer questions from the audience. Dr. Vitaly Shmatikov, Dr. Matt Fredrikson, and Dr. Arvind Narayanan bring their backgrounds in the Computer Science topics of security and privacy to the panel. Dr. Barbara Evans and Dr. Pilar Ossario, both hold Doctoral degrees in scientific fields in addition to law degrees this, along with extensive experience in the areas of privacy from a policy perspective allow them to bring unique insights regarding the use of medical and biological data as well as the regulatory policy issues associated with these types of data to the panel.

Small Talks about Big Data: Personalizing Breast Cancer - Integrating Predictive Phenotypes into Clinical Care Small Talk Video

Curious about how Big Data is being used to transform Science and Medicine? Then the Small Talks about Big Data Series is for you! Join Dr. Elizabeth Burnside as she shares her work to revolutionize breast cancer treatment using Big Data and her personal motivation for pursuing this important goal.

Small Talks about Big Data: Biomedical Big Data and the Computational Phenotyping Challenge Small Talk Video

Curious about how Big Data is being used to transform Science and Medicine? Then the Small Talks about Big Data Series is for you! Dr. Mark Craven, director of the Center for Predictive Computational Phenotyping (CPCP), describes what Computational Phenotyping is and delves into some of the challenges associated with this task. Find out how researchers at the CPCP use different types of biomedical Big Data to create new methods and discover ways to improve human health.

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