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Title:Automatic ICD Code Assignment to Medical Text with Semantic Relational Tuples
Author(s):Zhao, Sanqiang; He, Daqing; Zhang, Danchen; Li, Lei; Meng, Rui
Subject(s):International Classification of Disesases-9
ICD-9 Classification
Text mining
Electronic Medical Record (EMR) mining
Abstract:Mining the Electronic Medical Record (EMR henceforth) is growing in popularity but still lacks good methods for better understanding the text in EMR. One important task is assigning proper International Classification of Diseases (ICD henceforth, which is the code schema for EMR) code based on the narrative text of EMR document. For the task, we propose an automatic feature extraction method by means of capturing semantic relational tuples. We proved the semantic relational tuple is able to capture information at semantic level and it contribute to ICD-9 classification task in two aspects, negation identification and feature generation.
Issue Date:2017
Publisher:iSchools
Citation Info:Zhao, S., He, D., Zhang, D., Li, L. & Meng, R. (2017). Automatic ICD Code Assignment to Medical Text with Semantic Relational Tuples. In iConference 2017 Proceedings, Vol. 2 (pp. 156-158). https://doi.org/10.9776/17364
Series/Report:iConference 2017 Proceedings Vol. 2
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/98881
DOI:https://doi.org/10.9776/17364
Rights Information:Copyright 2017 is held by the authors
Date Available in IDEALS:2017-12-05


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