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

Yung Chun Chang, Hong Jie Dai, Johnny Chi Yang Wu, Jian Ming Chen, Richard Tzong Han Tsai, Wen Lian Hsu

研究成果: 雜誌貢獻文章同行評審

35 引文 斯高帕斯(Scopus)


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.
頁(從 - 到)S54-S62
期刊Journal of Biomedical Informatics
出版狀態已發佈 - 2013

ASJC Scopus subject areas

  • 電腦科學應用
  • 健康資訊學


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