title: Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives.
abstract: OBJECTIVE
  Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we developed and evaluated a system to automatically extract temporal expressions and events from clinical narratives. The extracted temporal expressions were additionally normalized by assigning type, value, and modifier.
  
  
  METHODS
  The system combines rule-based and machine learning approaches that rely on morphological, lexical, syntactic, semantic, and domain-specific features. Rule-based components were designed to handle the recognition and normalization of temporal expressions, while conditional random fields models were trained for event and temporal recognition.
  
  
  RESULTS
  The system achieved micro F scores of 90% for the extraction of temporal expressions and 87% for clinical event extraction. The normalization component for temporal expressions achieved accuracies of 84.73% (expression's type), 70.44% (value), and 82.75% (modifier).
  
  
  CONCLUSIONS
  Compared to the initial agreement between human annotators (87-89%), the system provided comparable performance for both event and temporal expression mining. While (lenient) identification of such mentions is achievable, finding the exact boundaries proved challenging.
  
  
  CONCLUSIONS
  The system provides a state-of-the-art method that can be used to support automated identification of mentions of clinical events and temporal expressions in narratives either to support the manual review process or as a part of a large-scale processing of electronic health databases.
authors.affiliation: Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia.
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Kovacevic
authors.forename: Azad
authors.initials: A
authors.lastname: Dehghan
authors.forename: Michele
authors.initials: M
authors.lastname: Filannino
authors.forename: John A
authors.initials: JA
authors.lastname: Keane
authors.forename: Goran
authors.initials: G
authors.lastname: Nenadic
mesh_terms: D001185:Artificial Intelligence; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D013995:Time; D057170:Translational Research, Biomedical
keywords: clinical NLP; clinical text mining; event extraction; termporal expression extraction; termporal expression normalization