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    政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 >  Item 140.119/75088


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/75088


    Title: Coreference resolution of medical concepts in discharge summaries by exploiting contextual information
    Authors: Lai, Po-Ting
    賴柏廷
    Chen, C.-Y.
    Dai, H.-J.
    Contributors: 資科系
    Keywords: accuracy;article;hospital discharge;hospital patient;information dissemination;medical information;model;natural language processing;patient discharge summary;artificial intelligence;automated pattern recognition;computer simulation;data mining;electronic medical record;evaluation;hospital discharge;human;methodology;multicenter study;natural language processing;semantics;United States;Artificial Intelligence;Computer Simulation;Data Mining;Electronic Health Records;Humans;Natural Language Processing;Patient Discharge;Pattern Recognition, Automated;Semantics;United States
    Date: 2012-09
    Issue Date: 2015-05-12 16:05:59 (UTC+8)
    Abstract: Objective: 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. Design: 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. Results: 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%). Conclusion: In this paper, the main challenges in the resolution of coreference relations in patient discharge summaries are described. Several rules are proposed to exploit contextual information, and three approaches presented. While single systems provided promising results, an ensemble approach combining the three systems produced a better performance than even the best single system.
    Relation: Journal of the American Medical Informatics Association, Volume 19, Issue 5, 2012, Pages 888-896
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1136/amiajnl-2012-000808
    DOI: 10.1136/amiajnl-2012-000808
    Appears in Collections:[資訊科學系] 期刊論文

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