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Enhancing Automatic ICD-9-CM Code Assignment for Medical Texts with PubMed

Zhang, Danchen and He, Daqing and Zhao, Sanqiang and Li, Lei Enhancing Automatic ICD-9-CM Code Assignment for Medical Texts with PubMed. In: BioNLP Workshop 2017. (In Press)

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Abstract

Assigning a standard ICD-9-CM code to disease symptoms in medical texts is an important task in the medical domain. Automating this process could greatly reduce the costs. However, the effectiveness of an automatic ICD-9-CM code classifier faces a serious problem, which can be triggered by unbalanced training data. Frequent diseases often have more training data, which helps its classification to perform better than that of an infrequent disease. However, a disease’s frequency does not necessarily reflect its importance. To resolve this training data shortage problem, we propose to strategically draw data from PubMed to enrich the training data when there is such need. We validate our method on the CMC dataset, and the evaluation results indicate that our method can significantly improve the code assignment classifiers' performance at the macro-averaging level.


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Details

Item Type: Conference or Workshop Item (Poster)
Status: In Press
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Zhang, Danchendaz45@pitt.eduDAZ45
He, Daqingdah44@pitt.eduDAH44
Zhao, Sanqiangsaz31@pitt.eduSAZ31
Li, Lei
Journal or Publication Title: BioNLP 2017
Event Title: BioNLP Workshop 2017
Event Type: Conference
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Additional Information: Date: 2017-05-22 (acceptance)
Date Deposited: 19 Jun 2017 14:23
Last Modified: 25 Aug 2017 04:55
URI: http://d-scholarship.pitt.edu/id/eprint/32401

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