title: Towards temporal relation discovery from the clinical narrative.
abstract: Disease progression and understanding relies on temporal concepts. Discovery of automated temporal relations and timelines from the clinical narrative allows for mining large data sets of clinical text to uncover patterns at the disease and patient level. Our overall goal is the complex task of building a system for automated temporal relation discovery. As a first step, we evaluate enabling methods from the general natural language processing domain - deep parsing and semantic role labeling in predicate-argument structures - to explore their portability to the clinical domain. As a second step, we develop an annotation schema for temporal relations based on TimeML. In this paper we report results and findings from these first steps. Our next efforts will scale up the data collection to develop domain-specific modules for the enabling technologies within Mayo's open-source clinical Text Analysis and Knowledge Extraction System.
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Guergana
authors.initials: G
authors.lastname: Savova
authors.forename: Steven
authors.initials: S
authors.lastname: Bethard
authors.forename: Will
authors.initials: W
authors.lastname: Styler
authors.forename: James
authors.initials: J
authors.lastname: Martin
authors.forename: Martha
authors.initials: M
authors.lastname: Palmer
authors.forename: James
authors.initials: J
authors.lastname: Masanz
authors.forename: Wayne
authors.initials: W
authors.lastname: Ward
mesh_terms: D018450:Disease Progression; D006801:Humans; D008722:Methods; D033262:Narration; D009323:Natural Language Processing; D012660:Semantics; D013995:Time