Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010.

Berry de Bruijn, Colin Cherry, Svetlana Kiritchenko, Joel D. Martin, Xiaodan Zhu

Abstract: Objective As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality. A critical part of that process is rigid benchmark testing of natural language processing methods on realistic clinical narrative. In this paper, the authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge.