A temporal constraint structure for extracting temporal information from clinical narrative

Li Zhou, Genevieve B. Melton, Simon Parsons, George Hripcsak

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Introduction: Time is an essential element in medical data and knowledge which is intrinsically connected with medical reasoning tasks. Many temporal reasoning mechanisms use constraint-based approaches. Our previous research demonstrates that electronic discharge summaries can be modeled as a simple temporal problem (STP). Objective: To categorize temporal expressions in clinical narrative text and to propose and evaluate a temporal constraint structure designed to model this temporal information and to support the implementation of higher-level temporal reasoning. Methods: A corpus of 200 random discharge summaries across 18 years was applied in a grounded approach to construct a representation structure. Then, a subset of 100 discharge summaries was used to tally the frequency of each identified time category and the percentage of temporal expressions modeled by the structure. Fifty random expressions were used to assess inter-coder agreement. Results: Six main categories of temporal expressions were identified. The constructed temporal constraint structure models time over which an event occurs by constraining its starting time and ending time. It includes a set of fields for the endpoint(s) of an event, anchor information, qualitative and metric temporal relations, and vagueness. In 100 discharge summaries, 1961 of 2022 (97%) identified temporal expressions were effectively modeled using the temporal constraint structure. Inter-coder evaluation of 50 expressions yielded exact match in 90%, partial match with trivial differences in 8%, partial match with large differences in 2%, and total mismatch in 0%. Conclusion: The proposed temporal constraint structure embodies a sufficient and successful implementation method to encode the diversity of temporal information in discharge summaries. Placing data within the structure provides a foundational representation upon which further reasoning, including the addition of domain knowledge and other post-processing to implement an STP, can be accomplished.

Original languageEnglish (US)
Pages (from-to)424-439
Number of pages16
JournalJournal of Biomedical Informatics
Volume39
Issue number4
DOIs
StatePublished - Aug 2006
Externally publishedYes

Bibliographical note

Funding Information:
This work was funded by National Library of Medicine grants R01 LM06910 “Discovering and applying knowledge in clinical databases”; R01 LM07659 “Capturing and Linking Genomic and Clinical Information;” and R01 LM07268 “Using Narrative Data to Enrich the Online Medical Record.”

Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.

Keywords

  • Discharge summary
  • Natural language processing
  • Simple Temporal Problem
  • Temporal constraints
  • Temporal model
  • Temporal reasoning
  • Temporal representation

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