BS, University of Connecticut
MALS, Wesleyan University
PhD (Information Science), University of Pittsburgh
Dr. Stephanie W. Haas is a Professor at the UNC School of Information and Library Science (SILS). Her research interests focus on the representation of information, and how representations enhance or impede work processes. More specifically, she is interested in natural language processing: what computers do with the language people use. Current and recent projects study these issues in collaboration with researchers from UNC's Schools of Medicine, Nursing, and Public Health, examining information representation in patient records, and its use for improving patient care. An award-winning teacher, she teach courses in Applications of Natural Language Processing, Systems Analysis, and Database Design (including an online version).
Courses Regularly Taught:
INLS 512 – Applications of Natural Language Processing
INLS 523 – Database 1 (online and face-to-face)
INLS 582 – Systems Analysis
Awards and Recognition:
2017, 2012, 2006, 1997 - Deborah Barreau Award for Teaching Excellence, School of Information and Library Science, University of North Carolina at Chapel Hill
2012 - Edward G. Holley for the Good of the Order Award, School of Information and Library Science, University of North Carolina at Chapel Hill
2005 - Francis Carroll McColl Term Professor, 2005 – 2007.
1996 - Outstanding Information Science Teacher of the Year, American Society for Information Science.
Selected Publications, Papers, Presentations:
Syndromic Surveillance using Emergency Department Records
Haas, S.W., Travers, D.T., Waller, A.E., Mahalingam, D., Crouch J., Schwartz, T. A. Mostafa, J. (2014). Emergency Medical Text Classifier: New system improves processing and classification of triage notes. Online Journal of Public Health Informatics, 6 (2).
Travers, D. A., Haas, S. W., Waller, A. E., Schwartz, T. A., Mostafa, J., Best, N. C., Crouch J. (2013). Implementation of Emergency Medical Text Classifier for syndromic surveillance. Proceedings of the American Medical Informatics Annual Symposium, 1365-74.
Travers, D., Haas, S. W., Waller, A., Crouch, J., Mostafa, J., Schwartz, T. (2010). Identifying evidence of fever in emergency department text. AMIA 2010 Annual Symposium, November 13-17, 2010, Washington, D.C. (poster)
Concept Extraction from Emergency Department Chief Complaint Text
Travers, D. A. & Haas, S. W. (2003). Using nurses’ natural language entries to build a concept-oriented terminology for patients’ chief complaints in the emergency department. Journal of Biomedical Informatics, 36(4-5), 260-270.
Travers, D. A. & Haas, S. W. (2004). Evaluation of Emergency Medical Text Processor, a system for cleaning chief complaint text data. Academic Emergency Medicine, 11(11), 1170-1176.
Haas, S. W., Travers, D. A., Tintinalli, J. E., Pollock, D., Waller, A., Barthell, E., et al. (2008). Towards Vocabulary Control for Chief Complaint. Academic Emergency Medicine, 15(5), 476-482.
Genre on the Web
Rosso, M. A. & Haas, S. W. (2010). Identification of Web Genres by User Warrant. In Genre on the Web: Computational Models and Empirical Studies. Mehler, A., Sharoff, S., Rehm, G. & Santini, M. (ed.) Springer.
Haas, S. W. & Grams, E. S. (2000). Readers, authors, and page structure: A discussion of four questions arising from a content analysis of Web pages. Journal of the American Society for Information Science, 51(2), 181-192.
Access and Use of Government Statistics
Marchionini, G., Haas, S. W., Zhang, J. & Elsas, J. (2005). Accessing government statistical information. Computer, 38(12), 52-61.
Hert, C. A., Denn, S. & Haas, S. W. (2004). The role of metadata in the statistical knowledge network: An emerging research agenda. Social Science Computer Review, 22(1), 92-99.
Haas, S. W. (2003). Improving the search environment: Informed decision making in the search for statistical information. Journal of the American Society for Information Science and Technology, 54(8), 782-797.
Video content: UNC SILS: On the Cutting Edge of Information Science