Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Genescene: biomedical text and data mining
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Extracting Biochemical Interactions from MEDLINE Using a Link Grammar Parser
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Extracting molecular binding relationships from biomedical text
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
The statistical significance of the MUC-4 results
MUC4 '92 Proceedings of the 4th conference on Message understanding
Classifying semantic relations in bioscience texts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
RelEx---Relation extraction using dependency parse trees
Bioinformatics
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Objective: We describe semantic relation (SR) classification on medical discharge summaries. We focus on relations targeted to the creation of problem-oriented records. Thus, we define relations that involve the medical problems of patients. Methods and materials: We represent patients' medical problems with their diseases and symptoms. We study the relations of patients' problems with each other and with concepts that are identified as tests and treatments. We present an SR classifier that studies a corpus of patient records one sentence at a time. For all pairs of concepts that appear in a sentence, this SR classifier determines the relations between them. In doing so, the SR classifier takes advantage of surface, lexical, and syntactic features and uses these features as input to a support vector machine. We apply our SR classifier to two sets of medical discharge summaries, one obtained from the Beth Israel-Deaconess Medical Center (BIDMC), Boston, MA and the other from Partners Healthcare, Boston, MA. Results: On the BIDMC corpus, our SR classifier achieves micro-averaged F-measures that range from 74% to 95% on the various relation types. On the Partners corpus, the micro-averaged F-measures on the various relation types range from 68% to 91%. Our experiments show that lexical features (in particular, tokens that occur between candidate concepts, which we refer to as inter-concept tokens) are very informative for relation classification in medical discharge summaries. Using only the inter-concept tokens in the corpus, our SR classifier can recognize 84% of the relations in the BIDMC corpus and 72% of the relations in the Partners corpus. Conclusion: These results are promising for semantic indexing of medical records. They imply that we can take advantage of lexical patterns in discharge summaries for relation classification at a sentence level.