title: Finding temporal order in discharge summaries.
abstract: A method for automatic analysis of time-oriented clinical narratives would be of significant practical import for medical decision making, data modeling and biomedical research. This paper proposes a robust corpus-based approach for temporal analysis of medical discharge summaries. We characterize temporal organization of clinical narratives in terms of temporal segments and their ordering. We consider a temporal segment to be a fragment of text that does not exhibit abrupt changes in temporal focus. Our method derives temporal order based on a range of linguistic and contextual features that are integrated in a supervised machine-learning framework. Our learning method achieves 83% F-measure in tempo-ral segmentation, and 78.3% accuracy in inferring pairwise temporal relations.
authors.affiliation: Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
authors.forename: Philip
authors.initials: P
authors.lastname: Bramsen
authors.forename: Pawan
authors.initials: P
authors.lastname: Deshpande
authors.forename: Yoong Keok
authors.initials: YK
authors.lastname: Lee
authors.forename: Regina
authors.initials: R
authors.lastname: Barzilay
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D010351:Patient Discharge; D013995:Time