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

Abstract：

: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.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Chang YC,Dai HJ,Wu JC,Chen JM,Tsai RT,Hsu WL

doi

10.1016/j.jbi.2013.09.007

subject

Has Abstract

pub_date

2013-12-01 00:00:00

pages

S54-62

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(13)00148-2

journal_volume

46 Suppl

pub_type

杂志文章

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