Abstract:
Correctly discovering causal relations from healthcare information can help people to understand disease mechanisms and discover disease causes. In some cases, the health...Show MoreMetadata
Abstract:
Correctly discovering causal relations from healthcare information can help people to understand disease mechanisms and discover disease causes. In some cases, the healthcare data do not follow a multivariate Gaussian distribution. We design a new causal structure learning algorithm. The algorithm can effectively combines ideas from local learning with simultaneous equations models techniques. In the first phase of the algorithm we select potential neighbors for each variable based on simultaneous equations models, and then perform a constrained hill-climbing search to orient the edges. Using the algorithm without prior knowledge, we analyze causal relations in the real data from the National Health and Nutrition Examination Survey.
Date of Conference: 18-21 December 2013
Date Added to IEEE Xplore: 06 February 2014
Electronic ISBN:978-1-4799-1309-1