Library Collaborative

A major CTSA-sponsored initiative involving CHIBI (through the EIRSL lab) and the Ehrman Medical Library is the Library-CHIBI Collaborative project that focuses on three main aims:

1. Development of the next generation Find-a-Researcher tool: This project seeks to develop and validate advanced technologies to identify research synergies and prospective collaborations between investigators within the institution to encourage new partnerships in interdisciplinary and translational research.

2. Deploy novel information retrieval models: The library provides an ideal environment for testing and refining information retrievals models developed within EIRSL. Such models were previously developed and validated in a lab settings and include more than 20 content and EBM-quality filters for searching and ranking MEDLINE articles according to content, format, and EBM methodological quality as well as filters to identify health-related websites with misleading information that may harm patients. Other models that will be deployed predict future citations counts of both newly-published and older articles as well as models for characterizing the nature of citations among articles.

3. Establish common web-based gateway for research community: CHIBI and the Ehrman Library will establish a definitive catalog of University-wide resources (such as core facilities, software packages, datasets, etc.). This catalog will also include resource searches (eg, biorepository samples), data, links to external databases and text analytics to search unstructured text contained in the literature.

References

"Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine." Y. Aphinyanaphongs, C.F. Aliferis. In Proceedings of the 2003 American Medical Informatics Association (AMIA) Annual Symposium, Washington, DC, USA; pages 31-35, 2003.
“Learning Boolean Queries for Article Quality Filtering”. Y. Aphinyanaphongs, C.F. Aliferis . In Proceedings of the 11th World Congress on Medical Informatics (MEDINFO), San Francisco, California, USA; September 7-11, 2004.
“Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine”. Y. Aphinyanaphongs, I. Tsamardinos, A. Statnikov, D. Hardin, C.F. Aliferis. J Am Med Inform Assoc., Mar-Apr;12(2):207-16, 2005.
"Extracting Drug-Drug Interaction Articles from MEDLINE to Improve the Content of Drug Databases". S. Duda, C.F. Aliferis, R.A. Miller, A. Statnikov, K.B.Johnson, Proc AMIA Symposium, 2005.
"Using citation data to improve retrieval from MEDLINE". E.V.Bernstam, J.R.Herskovic, Y. Aphinyanaphons, C.F.Aliferis, M.G.Sriram, W.R. Hersh. J Am Med Inform Assoc., Jan-Feb; 13(1):96-105, 2006 .
“Prospective validation of text categorization models for indentifying high-quality content-specific articles in PubMed”. Y. Aphinyanaphongs, C.F. Aliferis. Proc Annual Fall Conf AMIA, 2006.
“A Comparison of Citation Metrics to Machine Learning Filters for the Identification of High Quality MEDLINE Documents”. Y. Aphinyanaphongs, A. Statnikov, C.F. Aliferis. J Am Med Inform Assoc. Jul-Aug; 13(4):446-55, 2006.
“Text Categorization Models for Identifying Unproven Cancer Treatments on the Web” Y. Aphinanaphongs, C.F. Aliferis. In International Medical Informatics Congress, MEDINFO, 2007.
“A comparison of Impact Factor, Clinical Query Filters, and Pattern Recognition Query Filters in Terms of Sensitivity to Topic”. L. Fu, L. Wang, Y. Aphinyanaphongs, C. F. Aliferis. In International Medical Informatics Congress, MEDINFO, 2007.
“Models for Predicting and Explaining Citation Count of Biomedical Articles” Lawrence D. Fu, Constantin Aliferis. AMIA Fall Symposium 2008.
“Using Content-based and Bibliometric Features for Machine Learning Models to Predict Citation
Counts in the Biomedical Literature: L.D. Fu, C.F. Aliferis. (In preparation).
“Machine Learning Models for Automatic Classification of Instrumental Citations” L.D. Fu, C.F. Aliferis. (In preparation).