title: Eventual situations for timeline extraction from clinical reports.
abstract: OBJECTIVE
  To identify the temporal relations between clinical events and temporal expressions in clinical reports, as defined in the i2b2/VA 2012 challenge.
  
  
  METHODS
  To detect clinical events, we used rules and Conditional Random Fields. We built Random Forest models to identify event modality and polarity. To identify temporal expressions we built on the HeidelTime system. To detect temporal relations, we systematically studied their breakdown into distinct situations; we designed an oracle method to determine the most prominent situations and the most suitable associated classifiers, and combined their results.
  
  
  RESULTS
  We achieved F-measures of 0.8307 for event identification, based on rules, and 0.8385 for temporal expression identification. In the temporal relation task, we identified nine main situations in three groups, experimentally confirming shared intuitions: within-sentence relations, section-related time, and across-sentence relations. Logistic regression and Naïve Bayes performed best on the first and third groups, and decision trees on the second. We reached a 0.6231 global F-measure, improving by 7.5 points our official submission.
  
  
  CONCLUSIONS
  Carefully hand-crafted rules obtained good results for the detection of events and temporal expressions, while a combination of classifiers improved temporal link prediction. The characterization of the oracle recall of situations allowed us to point at directions where further work would be most useful for temporal relation detection: within-sentence relations and linking History of Present Illness events to the admission date. We suggest that the systematic situation breakdown proposed in this paper could also help improve other systems addressing this task.
authors.affiliation: LIMSI-CNRS, Orsay, France.
authors.forename: Cyril
authors.initials: C
authors.lastname: Grouin
authors.forename: Natalia
authors.initials: N
authors.lastname: Grabar
authors.forename: Thierry
authors.initials: T
authors.lastname: Hamon
authors.forename: Sophie
authors.initials: S
authors.lastname: Rosset
authors.forename: Xavier
authors.initials: X
authors.lastname: Tannier
authors.forename: Pierre
authors.initials: P
authors.lastname: Zweigenbaum
mesh_terms: D001185:Artificial Intelligence; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D013995:Time
keywords: Chronology as Topic; Information Extraction; Medical Records; Natural Language Processing; Text Mining