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Computational Data Mining For Feature Extraction In Health Informatics

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2009 Annual Conference & Exposition


Austin, Texas

Publication Date

June 14, 2009

Start Date

June 14, 2009

End Date

June 17, 2009



Conference Session

Engineering and Other Disciplines

Tagged Division

Multidisciplinary Engineering

Page Count


Page Numbers

14.351.1 - 14.351.16



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Paper Authors


Mahmoud Quweider University of Texas, Brownsville

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Dr. M K Quweider is an Associate Professor in the Computer & Information Sciences at the University of Texas at Brownsville/Texas Southmost College. He received his Ph.D. in Engineering Science and an M.S. in Applied Mathematics, M.S. in Engineering Science, and M.S. in Biomedical Engineering all from the University of Toledo, Ohio. After graduation, he worked at several places including Pixera, a digital multimedia processing company in Cupertino, CA, and 3COM, a networking and communication company in Schaumberg, IL. He joined the UTB/TSC in 2000. His areas of interest include Imaging, Visualization and Animation, Web Design and Graphics.

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Adriana Perez University of Texas, Brownsville

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Gabriala Oropeza University of Texas, Brownsville


Juan Iglesias University of Texas, Brownsville

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Dr. J R Iglesias is the Chair and Associate professor in the Computer & Information Sciences at University of Texas at Brownsville/Texas Southmost College. He received his Ph.D. in Computer Science from New Mexico State University (NMSU), New Mexico, USA, with specialization in Databases, and the B.Sc and M.S. in Computer Science from the National Autonomous University of Mexico. He has worked as an Associate Director for the Federal Electoral Institute (IFE), Mexico City, Mexico during the 1997 year. His areas of interest include Databases, Programming Languages, Data mining, Web Design, and e-Commerce Systems.

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Computational Data Mining for Feature Extraction in Health Informatics

Abstract This paper presents the methodologies and lessons learned from a cooperative effort within our institution involving the Health Science Center faculty, the Computer Science faculty, and senior/graduate students; the effort aimed at building a data mining module to input, process, and extract relevant information related to a pilot study on the effects of to Endocrine Disrupting Chemicals (EDCs) exposure on pregnancy, which was conducted by the Health Center and the School of Public Health.

Interdisciplinary in nature, the project brought together biostatisticians, medical doctors, and computer and information scientists (CIS). On the medical side, the team was trying to assess human health risks from exposures to Endocrine Disrupting Chemicals, measuring both the exposure level and its ramifications in pregnant women of the Rio Grande Valley. To aid in the process from a computational and engineering point of view, a professor and two computer science and engineering majors were put in charge of taking the requirements and specifications from the medical side and converting them into a robust and flexible software application with a friendly graphical user interface.

The study has progressed in a sequence of phases that included: obtaining approval of human subject research; preparing the necessary paperwork; recruiting subjects at local clinics, collecting blood and tissue samples, performing blood and tissue samples analysis, coding and entering data, constructing an integrated data base, performing statistical analysis, assessing human risk, and mining the database for trends, anomalies, and unusual cases.

The educational experience and the interaction between the students and the medical/health team were invaluable. The CIS students, and their professors, benefited immensely from not only coding the design and requirements but also from learning about concepts such as getting a certificate of training on research on human subjects, conducting and inscribing surveys, extracting and visualizing basic factors and trends from the collected data.

Our paper details the students’ academic and professional experience in working with a real-life project with profound health and social impact on their local community. It also lays the foundation for continuous collaboration involving faculty and students between the involved departments.

Introduction Computer Science is an applied science by its nature. Its applications are seen everywhere such as in the Internet, communications, e-commerce business to business and business to customer systems, electronics, and medical devices just to name few. This wide-spread range of applications brings a major challenge to computer science: the need to collaborate with other

Quweider, M., & Perez, A., & Oropeza, G., & Iglesias, J. (2009, June), Computational Data Mining For Feature Extraction In Health Informatics Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. 10.18260/1-2--4716

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