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Abstract: The biomedical sciences and healthcare are contributing significantly to the big data revolution through advances in genomic sequencing technology and imaging, clinical and personally-generated data. Data mining and machine learning techniques have played an increasingly important role in medical informatics with the goal of discovering knowledge and insights from various data sources. Causal inference is an important methodological pool from which one can draw powerful techniques for knowledge discovery and data-driven insights. Causal discovery methods were developed to address the financial and ethical concerns associated with randomized controlled trials. An attestation to their significance is that they have been recognized with both the Turing Award (to Judea Pearl) and the Nobel Prize (to Clive Granger). Discovery of causality is a major goal in basic, translational and clinical science. In computational biology, neuroscience, epidemiology and biomedicine one often faces the daunting task of finding causal relationships in very large-dimensional data. This highlights the necessity to develop and evaluate algorithms and tools to improve the current state of the art in causal discovery from experimental, quasi-experimental and non-experimental (i.e., observational) data. The main theme of the workshop this year is causal inference for health data analytics, which aims to address both the theoretical and experimental underpinnings of these methods. This includes development and applications of the methods and discussions on how to make them practically useful to clinicians, patients and other healthcare stakeholders. This topic is timely and has received a lot of interest recently. We would like to invite researchers from both academia and industry who are interested in this topic to participate in this workshop, share their opinions and experience, as well as discuss future directions.

Learning Objective 1: Promote better collaboration between data mining experts and clinical practitioners.
Discuss the areas of healthcare in which better tools and data mining techniques are needed.
Help the data mining community receive feedback and suggestions from expert physicians.
Build a community amongst researchers and expert clinicians focusing on data mining in medical informatics.
Discuss new data mining opportunities to leverage open data in the healthcare space.

Authors:

Kenney Ng (Presenter)
IBM Research

Bisakha Ray, NYU
Sisi Ma, NYU
Kun Zhang, CMU
Fei Wang, Weill Cornell

Presentation Materials:

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