Journal of Biomedical Informatics - Special issue: Unified medical language system
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
High throughput modularized NLP system for clinical text
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Automatic extraction of hierarchical relations from text
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
SVM based learning system for information extraction
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Methodological Review: What can natural language processing do for clinical decision support?
Journal of Biomedical Informatics
Extracting information for generating a diabetes report card from free text in physicians notes
Louhi '10 Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents
A hybrid approach for the extraction of semantic relations from MEDLINE abstracts
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
| Hi-index | 0.00 |
The Clinical E-Science Framework (CLEF) project has built a system to extract clinically significant information from the textual component of medical records, for 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 approaches are now routinely applied to relationship extraction. We report on the novel application of these statistical techniques to clinical relationships. We describe a supervised machine learning system, trained with a corpus of oncology narratives hand-annotated with clinically important relationships. Various shallow features are extracted from these texts, and used to train statistical classifiers. We compare the suitability of these features for clinical relationship extraction, how extraction varies between inter- and intra-sentential relationships, and examine the amount of training data needed to learn various relationships.