Title
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.
Year
DOI
Venue
2013
10.1016/j.jbi.2013.09.007
Journal of Biomedical Informatics
Keywords
Field
DocType
hybrid method,tempting system,stage-based system,tlink track dataset,tlink candidate,natural language processing,stage-based tempting system,baseline system,tlink type,training set,patient discharge summary,temporal relation extraction,text mining,integration algorithm,maximum entropy
Data mining,Ranking,Computer science,Timeline,Patient Discharge Summaries,Artificial intelligence,Principle of maximum entropy,Fuse (electrical),Cross-validation,Machine learning,Test set,Relationship extraction
Journal
Volume
Issue
ISSN
46 Suppl
6
1532-0480
Citations 
PageRank 
References 
4
0.47
23
Authors
6
Name
Order
Citations
PageRank
Yung-Chun Chang18719.78
Hong-Jie Dai228821.58
Johnny Chi-Yang Wu380.91
Jian-Ming Chen440.47
Richard Tzong-Han Tsai571454.89
Wen-Lian Hsu61701198.40