Mining clinical relationships from patient narratives

Background: The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records in order to support clinical research, evidence-based healthcare and genotype-meets-phenotype informatics. One part of this system is the identification of relationships between clinically important entities in the text. Typical approaches to relationship extraction in this domain have used full parses, domain-specific grammars, and large knowledge bases encoding domain knowledge. In other areas of biomedical NLP, statistical machine learning (ML) approaches are now routinely applied to relationship extraction. We report on the novel application of these statistical techniques to the extraction of clinical relationships. Results: We have designed and implemented an ML-based system for relation extraction, using support vector machines, and trained and tested it on a corpus of oncology narratives handannotated with cl...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Angus Roberts, Robert J. Gaizauskas, Mark Hepple, Yikun Guo
• Keywords: BMCBI 2008, Clinical E-Science Framework, Clinical Relationship, Relation Extraction