Cross-narrative temporal ordering of medical events

Preethi Raghavan, Eric Fosler-Lussier, Noémie Elhadad, Albert M. Lai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Cross-narrative temporal ordering of medical events is essential to the task of generating a comprehensive timeline over a patient's history. We address the problem of aligning multiple medical event sequences, corresponding to different clinical narratives, comparing the following approaches: (1) A novel weighted finite state transducer representation of medical event sequences that enables composition and search for decoding, and (2) Dynamic programming with iterative pairwise alignment of multiple sequences using global and local alignment algorithms. The cross-narrative coreference and temporal relation weights used in both these approaches are learned from a corpus of clinical narratives. We present results using both approaches and observe that the finite state transducer approach performs performs significantly better than the dynamic programming one by 6.8% for the problem of multiple-sequence alignment.

Original languageEnglish
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages998-1008
Number of pages11
ISBN (Print)9781937284725
DOIs
StatePublished - 2014
Externally publishedYes
Event52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Baltimore, MD, United States
Duration: Jun 22 2014Jun 27 2014

Publication series

Name52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference
Volume1

Conference

Conference52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014
CountryUnited States
CityBaltimore, MD
Period06/22/1406/27/14

Fingerprint Dive into the research topics of 'Cross-narrative temporal ordering of medical events'. Together they form a unique fingerprint.

Cite this