In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
will defend his dissertation proposal
Association Rule Mining for Risk Assessment in Epidemiology
In epidemiology, a risk assessment measures the association between exposures and a health outcome of interest. Risk characterization has been traditionally performed using statistical methods, but they have limitations, such as difficulty in handling highly correlated variables and in assessing synergic actions between exposures.
These limitations become evident in studies related to asthma. Asthma is a chronic respiratory disease with increasing prevalence in the population. The causes of this increment are uncertain. Many factors have been associated with causing and triggering asthma, but their interactions, as well as which one is the main responsible for the spreading of asthma, are still unclear. Outdoor air pollution is on the list of suspects. Characterizing the connection between asthma and air pollution is challenging, because of high collinearity between pollutant agents, possible synergic actions, and difficulty in controlling the exposure. The research community is currently encouraging the use of multi-pollutant models to get better results.
We propose a novel algorithm for association rule mining modified to find connections between exposures and risk variations. The algorithm has been tested on synthetic data and on real collection of data about pediatric asthma cases and pollution levels in Houston. Preliminary results and future work will be discussed.
Date: Thursday, May 14, 2015
Time: 9:00 AM
Place: HBS 350
Advisor: Prof. Ricardo Vilalta
Faculty, students, and the general public are invited.