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Research

Castellanos, A. 2015 “Relevance is in the Eye of the Beholder: Design Principles for the Extraction of Context-Aware Information from Healthcare Data” presented at the Workshop on Information Technologies and Systems (WITS) 2015 as a dissertation proposal.
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Tremblay, M. C., Parra, C., and Castellanos, A. 2015. “Corporate Social Responsibility Reports: Understanding Topics via Text Mining” presented at Americas Conference on Information Systems (AMCIS) 2015 (Puerto Rico).
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Tremblay, M. C., Parra, C., and Castellanos, A. 2014 “Analyzing Corporate Social Responsibility Reports using Unsupervised and Supervised Text Data Mining” presented at the Design Science Research in Information Systems (DESRIST) Conference 2015 (Dublin, Ireland).
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Castillo, A., Castellanos, A., & Tremblay, M. C. (2014). Improving Case Management via Statistical Text Mining in a Foster Care Organization. In Advancing the Impact of Design Science: Moving from Theory to Practice (pp. 312-320). Springer International Publishing. Presented at DESRIST 2014 (Miami, FL).
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Castellanos, A., Castillo, A., and VanderMeer, D. 2013. ExUp: Inferring Multiple-Dimension Rating System” Presented in WITS, Milan, Italy, December 2013.
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Castellanos, A., Castillo, A., Gudi, A., & Lee, R. Decision Making Drivers and Preparedness in Emergency Planning: A Case Study. (WIT Press).
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Work-In-Progress


Castellanos, A., Castillo, A.,Tremblay, M.C, Vandermeer, D., & Lukyanenko, R. “Identifying Institutional Documentation Style To Improve Business Processes” targeted for MIS Quarterly.

Models of Optimal Classification to Support Open-World Information Systems. Targeted for ACM TMIS.

Castellanos, A., Castillo, A., Tremblay, M. C., & Lukyanenko, R. Fueling the Manual Process: Data Mining a “Gold Standard” in Case Management.

Castillo, A., Castellanos, A., & VanderMeer, D. “A Recommendation System for Multi-Valued, Non-Parametric Data.” Targeted for Decision Support Systems.

Tremblay, M. C., Parra, C., and Castellanos, A. “Corporate Social Responsibility Reports: Understanding Topics via Text Mining” targeted for Decision Sciences Journal

Parra, C., Tremblay, M.C. ,Paul, K and Castellanos, A. “Environmental Sustainability Embeddedness in Core Business Discourse: Evidence using Text Data Mining on Corporate Sustainability Reports”

Aguirre-Ureta, M., Marakas, G., & Castellanos, A. “When More Isn’t Necessarily Better: Conceptualization and Use of the Experience Construct in IS Research”.


Posters, Prototypes, and Panels


Castellanos, A., Lukyanenko, R., & Tremblay, M. C.,  (2016). Towards Goal-Aware Information Retrieval. Submitted to the 25th International World Wide Web Conference (WWW, 2016) (Montreal, Canada). 

Castellanos, A., Castillo, A., Tremblay, M. C., & Lukyanenko, R. (2016). Enhancing a “Gold Standard” in Case Management. Submitted to ACM-Digital Health (Montreal, Canada).

Castellanos, A., Castillo, A., & Tremblay, M. C. (2014). Improving Case Management via Statistical Text Mining in a Foster Care Organization. Presented at AMIA (Washington, D.C.)

Tremblay, M., Castellanos, A., & Deckard, G. (2012, December). Research Prototype: A Knowledge Management System to track the Evaluation of the Implementation of a Statewide Health Information Exchange. Workshop on Information Technologies and Systems (WITS), Orlando, Florida. Best prototype runner-up.

Panelist at the 3rd annual South Florida Healthcare Trade Fair & Regional Conference sponsored by the Healthcare Information and Management Systems Society (HIMSS) South Florida Chapter. The panel was named: South Florida Health Information Exchange (HIE) initiatives – leadership update and perspectives on HIE. The panelists included the Coordinator for Adoption of the Florida HIE, the CIO of Memorial Healthcare System, and a member of the South Florida Regional Extension Center.


Dissertation Proposal


Title: “Design Principles for the Extraction of Context-Aware Information”

ABSTRACT

In the course of normal business operations, organizations generate and store transactional data to cover different tactical needs. These data may serve different purpose and the information needs of a user vary based on the user’s background and/or task at hand. Our goal is to propose design principles in order to abstract context information from stored data. To do so, we propose two different case studies in a healthcare setting. In the first case study we use a domain-specific ontology to abstract context-aware information from structured data. In the second case study we use knowledge discovery techniques to extract context-aware information from unstructured data. The results of these studies have implications both to theory and practice. From a practical standpoint, context-aware systems reduce the irrelevance of the information presented to the user, allowing for effective use of resources, which are critical in healthcare. From a theory perspective we validate a domain-specific ontology and we extend the concept of information modularity by adopting ontological principles to cover different information needs.