Coreference resolution of medical concepts in discharge summaries by exploiting contextual information.

Journal of the American Medical Informatics Association : JAMIA
Hong-Jie DaiWen-Lian Hsu

Abstract

Patient discharge summaries provide detailed medical information about hospitalized patients and are a rich resource of data for clinical record text mining. The textual expressions of this information are highly variable. In order to acquire a precise understanding of the patient, it is important to uncover the relationship between all instances in the text. In natural language processing (NLP), this task falls under the category of coreference resolution. A key contribution of this paper is the application of contextual-dependent rules that describe relationships between coreference pairs. To resolve phrases that refer to the same entity, the authors use these rules in three representative NLP systems: one rule-based, another based on the maximum entropy model, and the last a system built on the Markov logic network (MLN) model. The experimental results show that the proposed MLN-based system outperforms the baseline system (exact match) by average F-scores of 4.3% and 5.7% on the Beth and Partners datasets, respectively. Finally, the three systems were integrated into an ensemble system, further improving performance to 87.21%, which is 4.5% more than the official i2b2 Track 1C average (82.7%). In this paper, the main challe...Continue Reading

References

Jun 21, 2011·Journal of the American Medical Informatics Association : JAMIA·Özlem UzunerScott L DuVall
Mar 1, 2012·Journal of the American Medical Informatics Association : JAMIA·Ozlem UzunerBrett R South

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Citations

Jun 3, 2014·BMC Bioinformatics·Richard Tzong-Han Tsai, Po-Ting Lai
Dec 5, 2013·Journal of the American Medical Informatics Association : JAMIA·Jyotishman PathakJoshua C Denny
Apr 9, 2013·Journal of Biomedical Informatics·Md Faisal Mahbub Chowdhury, Pierre Zweigenbaum
Jan 10, 2013·PloS One·Jongkwang KimKai Tan
Aug 22, 2017·Journal of Biomedical Informatics·Hong-Jie DaiShabbir Syed-Abdul

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