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Author Laura, Eitel J M ♦ March, Alan D
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2011
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword ICD9-CM ♦ Automated coding ♦ Data quality ♦ Shrinkage ♦ Text classification
Abstract This article analyzes the data quality issues that emerge when training a shrinkage-based classifier with noisy data. A statistical text analysis technique based on a shrinkage-based variation of multinomial naive Bayes is applied to a set of free-text discharge diagnoses occurring in a number of hospitalizations. All of these diagnoses were previously coded according to the Spanish Edition of ICD9-CM. We deal with the issue of analyzing the predictive power and robustness of the statistical machine learning algorithm proposed for ICD-9-CM classification. We explore the effect of training the models using both clean and noisy data. In particular our work investigates the extent to which errors in free-text diagnoses propagate to the classification model. A measure of predictive accuracy is calculated for the text classification algorithm under analysis. Subsequently, the quality of the sample data is incrementally deteriorated by simulating errors in the text and/or codes. The predictive accuracy is recomputed for each of the noisy samples for comparison purposes. Our research shows that the shrinkage-based classifier is a valid alternative to automate ICD9-CM coding even under circumstances in which the quality of the training data is in question.
ISSN 19361955
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2011-12-01
Publisher Place New York
e-ISSN 19361963
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 2
Issue Number 3
Page Count 22
Starting Page 1
Ending Page 22

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Source: ACM Digital Library