Automatic lymphoma classification with sentence subgraph mining from pathology reports.

CONCLUSIONS: We compare our system with three baseline classifiers using standard n-grams, full MetaMap concepts, and filtered MetaMap concepts. Our system achieves high F-measures on multiple binary classifications of lymphoma (Burkitt lymphoma, 0.8; diffuse large B-cell lymphoma, 0.909; follicular lymphoma, 0.84; Hodgkin lymphoma, 0.912). Significance tests show that our system outperforms all three baselines. Moreover, feature analysis identifies subgraph features that contribute to improved performance; these features agree with the state-of-the-art knowledge about lymphoma classification. We also highlight how these unsupervised relation features may provide meaningful insights into lymphoma classification. PMID: 24431333 [PubMed - as supplied by publisher]
Source: Journal of the American Medical Informatics Association - Category: Information Technology Authors: Tags: J Am Med Inform Assoc Source Type: research