Concept extraction Radiology reports Medical reports Clinical narratives

Conditional random fields a b s t r a c t We discuss the problem of performing information extraction from free-text radiology reports via supervised learning. In this task, segments of text (not necessarily coinciding with entire sentences, and possibly crossing sentence boundaries) need to be annotated with tags representing concepts of interest in the radiological domain. In this paper we present two novel approaches to IE for radiology reports: (i) a cascaded, two-stage method based on pipelining two taggers generated via the well known linear-chain conditional random fields (LC-CRFs) learner and (ii) a confidence-weighted ensem-ble method that combines standard LC-CRFs and the proposed two-stage method. We also report on the use of ‘‘positional features’’, a novel type of feature intended to aid in the automatic annotation of texts in which the instances of a given concept may be hypothesized to systematically occur in spe-cific areas of the text. We present experiments on a dataset of mammography reports in which the pro-posed ensemble is shown to outperform a traditional, single-stage CRFs system in two different, applicatively interesting scenarios.! 2013 Elsevier Inc. All rights reserved. 1