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Description

Predictive modeling has become an important tool in biomedical and health science over the past few years. Applications of predictive modeling in biomedical and health science span a wide range. However, developing high quality, generalizable, and easily deployed models for biomedical and health science applications remains challenging especially when using Big Data. The workshop will address how causal feature selection methods tackle the feature selection problem, a critical component of predictive modeling. Both theoretical and practical aspects of causal feature selection will be addressed with lectures and hands-on tutorial with real biomedical data.

Learning Objective 1: Using causal feature selection to enhance predictive modeling for Big Data health data science

Authors:

Sisi Ma (Presenter)
University of Minnesota

Alexander Statnikov (Presenter)
New York University

Constantin Aliferis (Presenter)
University of Minnesota

Presentation Materials:

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