Cross-sectional relatedness between sentences in breast radiology reports: development of an SVM classifier and evaluation against annotations of five breast radiologists.

ABSTRACT: Introduce the notion of cross-sectional relatedness as an informational dependence relation between sentences in the conclusion section of a breast radiology report and sentences in the findings section of the same report. Assess inter-rater agreement of breast radiologists. Develop and evaluate a support vector machine (SVM) classifier for automatically detecting cross-sectional relatedness. A standard reference is manually created from 444 breast radiology reports by the first author. A subset of 37 reports is annotated by five breast radiologists. Inter-rater agreement is computed among their annotations and standard reference. Thirteen numerical features are developed to characterize pairs of sentences; the optimal feature set is sought through forward selection. Inter-rater agreement

SUBMITTER: Sevenster M 

PROVIDER: S-EPMC3782592 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature