Enhancing Bayesian causal models using prior expertise from ontologies, for
patients of Alzheimer's Disease. 
   Lead Investigator:    Hengyi Hu
   Institution      :    George Mason University
   E-Mail           :    hengyihu@gmail.com
   Proposal ID      :    1582



   Proposal Description:
   The research using this dataset has two primary aims: 1) to create a causal   model of symptoms experienced by Alzheimer's patients using the Min-Max Hill      Climbing algorithm, and 2) to improve this model using prior expertise found in   authoritative medical ontologies, such as MedDRA, ICD-10-CM, and SNOMED CT.       This is a novel method meant to go beyond what our current learning algorithms can provide us, and tap into a pre-existing source of expertise for a disease.    The hypothesis is that the prior expertise in an ontology can be used to orient   the causal Bayesian networks, creating a model that better fits the data. For     example, in SNOMED CT, alcohol and the consumption of alcohol is a cause of       anxiety. Knowing this causal relationship, the model learned from data can be     constrained. Knowing several of these relationships would alter the structure     of the network, and force the algorithm to consider prior expertise before        fitting the data to the structure. We can compare these causal models, and        confirm the changes of the structure using existing epidemiological literature.   Successfully comparing the different models, we will have identified a way to     create a better causal model that does not depend solely on the complexity of     the algorithm. Instead, the model will be based on existing expertise and show    that statistical methods and complex algorithms only take us so far when          determining causality.