Clinical E-Science Framework: Sheffield NLP

Clinical E-Science Framework

University of Sheffield

Brief Description of the CLEF Project:

CLEF aims to develop a high quality, secure and interoperable information repository, derived from operational electronic patient records to enable ethical and user-friendly access to patient information in support of clinical care and biomedical research (Full Project Proposal, Executive Summary).

CLEF Project Partners:

University of Manchester: Medical Informatics Group
University College London: Centre for Health Informatics and Multiprofessional Education
Royal Marsden Hospital: Information Directorate
University of Cambridge: Judge Institute of Management
University of Sheffield: Natural Language Processing Group
Open University: Computing Department

Funding and Duration of the CLEF Project:

Funded by Medical Research Council (MRC)
•2003 – 2005 (CLEF), 2005 – 2007 (CLEF-Services)

Sheffield NLP's Role the CLEF Project:

Well-founded clinical studies require access to extensive, fine-grained data about individual patients. The bulk of this information is held in textual form in clinical reports, e.g. discharge summaries, radiology and pathology reports. Using generic Information Extraction technology specialised for work in bioinformatics applications, the Natural Language Processing Group at the University of Sheffield will develop tools to automatically identify, extract and markup key information in clinical reports. Specifically we shall extract the diagnosis, stage, and treatment intent from the patient summaries.

CLEF Project Members at the University of Sheffield:

Resources:

CLEF Gold Standard Corpus:
In order to evaluate our information extraction system to extract the clinically significant information from clinical texts, we created the CLEF gold standard corpus. It contains 167 clinical documents, chosen from 565K CLEF corpus. More detailed information about the annotation guildelines, the corpus and the tools used for the annotation exercises can be found in the link above.

Papers

A. Roberts and R. Gaizauskas and M. Hepple and Y. Guo. 2008. Combining terminology resources and statistical methods for entity recognition: an evaluation. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC 2008).
A. Roberts and R. Gaizauskas and M. Hepple and G. Demetriou and Y. Guo and A. Setzer. 2008.Semantic Annotation of Clinical Text: The CLEF Corpus. In Proceedings of the LREC 2008 Workshop on Building and Evaluating Resources for Biomedical Text Mining.
A. Roberts and R. Gaizauskas and M. Hepple. 2008. Extracting Clinical Relationships from Patient Narratives. In Proceedings of the ACL Workshop on Current Trends in Biomedical Natural Language Processing.
A. Roberts and R. Gaizauskas and M. Hepple and N. Davis and G. Demetriou and Y. Guo and J. Kola and I. Roberts. et al. 2007. The CLEF Corpus: Semantic Annotation of Clinical Text. In American Medical Informatics Association 2007 Proceedings. Biomedical and Health Informatics: From Foundations to Applications to Policy.
Y. Guo and R. Gaizauskas and I. Roberts and G. Demetriou and M. Hepple. 2006. Identifying Personal Health Information Using Support Vector Machines. In Proceedings of the AMIA 2006 Workshop on Challenges in Natural Language Processing for Clinical Data.
R. Gaizauskas and H. Harkema and M. Hepple and A. Setzer. 2006. Task-Oriented Extraction of Temporal Information: The Case of Clinical Narratives. In Proceedings of the 13th International Symposium on Temporal Representation and Reasoning (TIME2006).
H. Harkema, A. Setzer, R. Gaizauskas, M. Hepple, R. Power, J. Rogers. 2005. Mining and Modelling Temporal Clinical Data. In: Proceedings of the 4th UK e-Science All Hands Meeting, Nottingham, UK. S.J. Cox (ed.). EPSRC.
H. Harkema, I. Roberts, R. Gaizauskas, M. Hepple. 2005. Information Extraction from Clinical Records. In: Proceedings of the 4th UK e-Science All Hands Meeting, Nottingham, UK. S.J. Cox (ed.). EPSRC.
H. Harkema, I. Roberts, R. Gaizauskas, M. Hepple. 2005. A Web Service for Biomedical Term Look-up. In: Comparative and Functional Genomics, Volume 6, Issue 1-2 (p 86-93). Try it!
H. Harkema, R. Gaizauskas, M. Hepple, A. Roberts, I. Roberts, N. Davis, Y. Guo. 2004. A Large Scale Terminology Resource for Biomedical Text Processing. In: Proceedings of Linking Biological Literature, Ontologies, and Databases Workshop, NAACL/HLT 2004, Boston, USA (poster).
R. Gaizauskas, M. Hepple, N. Davis, Y. Guo, H. Harkema. A. Roberts, I. Roberts, 2003. AMBIT: Acquiring Medical and Biological Information from Text. In: Proceedings of the 2nd UK e-Science All Hands Meeting, Nottingham, UK. S.J. Cox (ed.). EPSRC.

More information:

Last update: 25/09/08, Yikun Guo