TEMPTING system: A hybrid method of rule and machine learning for temporal relation extraction in patient discharge summaries

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Title:	TEMPTING system: A hybrid method of rule and machine learning for temporal relation extraction in patient discharge summaries
Authors:	蔡宗翰;Chang, Yung-Chun;Dai, Hong-Jie;Wu, Johnny Chi-Yang;Chen, Jian-Ming;Tsai, Richard Tzong-Han;Hsu, Wen-Lian
Contributors:	資訊電機學院資訊工程學系
Keywords:	Algorithms;Data Mining - methods;Databases, Factual;Electronic Health Records;Humans;Hybrid method;Maximum entropy;Medical Informatics - methods;Natural Language Processing;Patient Discharge Summaries;Reproducibility of Results;Temporal relation extraction;Text mining;Time Factors
Date:	2013-01-01
Issue Date:	2026-04-23 14:09:42 (UTC+8)
Publisher:	Academic Press Inc.;United States: Elsevier Inc
Abstract:	摘要： [Display omitted] •We developed rules and maximum entropy models for extracting temporal relation.•We developed an algorithm to hybridize results of rules and maximum entropy models.•We identified required knowledge and inference for temporal relations extraction.•Clinical note sections within a discharge summary can help define temporal relations. Patient discharge summaries provide detailed medical information about individuals who have been hospitalized. To make a precise and legitimate assessment of the abundant data, a proper time layout of the sequence of relevant events should be compiled and used to drive a patient-specific timeline, which could further assist medical personnel in making clinical decisions. The process of identifying the chronological order of entities is called temporal relation extraction. In this paper, we propose a hybrid method to identify appropriate temporal links between a pair of entities. The method combines two approaches: one is rule-based and the other is based on the maximum entropy model. We develop an integration algorithm to fuse the results of the two approaches. All rules and the integration algorithm are formally stated so that one can easily reproduce the system and results. To optimize the system’s configuration, we used the 2012 i2b2 challenge TLINK track dataset and applied threefold cross validation to the training set. Then, we evaluated its performance on the training and test datasets. The experiment results show that the proposed TEMPTING (TEMPoral relaTion extractING) system (ranked seventh) achieved an F-score of 0.563, which was at least 30% better than that of the baseline system, which randomly selects TLINK candidates from all pairs and assigns the TLINK types. The TEMPTING system using the hybrid method also outperformed the stage-based TEMPTING system. Its F-scores were 3.51% and 0.97% better than those of the stage-based system on the training set and test set, respectively. 其他題名： J Biomed Inform 出版者： United States: Elsevier Inc 出版日期： 2013-12-01 出處： Journal of biomedical informatics, 2013-12, Vol.46, p.S54-S62 資源來源： Elsevier ScienceDirect Journals 版權： 2013 Elsevier Inc. 版權： Copyright © 2013 Elsevier Inc. All rights reserved. 識別號： ISSN: 1532-0464 識別號： ISSN: 1532-0480 識別號： EISSN: 1532-0480 識別號： DOI: 10.1016/j.jbi.2013.09.007 識別號： PMID: 24060600
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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