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Scientific article
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English

From episodes of care to diagnosis codes: automatic text categorization for medico-economic encoding

Published inAMIA ... Annual Symposium proceedings, p. 636-640
Publication date2008
Abstract

We report on the design and evaluation of an original system to help assignment ICD (International Classification of Dis-ease) codes to clinical narratives. The task is defined as a multi-class multi-document classification task. We combine a set of machine learning and data-poor methods to generate a single automatic text categorizer, which returns a ranked list of ICD codes. The combined ranking system currently ob-tains a precision of 75% at high ranks and a recall of about 63% for the top twenty returned codes for a theoretical upper bound of about 79% (inter-coder agreement). The performance of the data-poor classifier is weak, whereas the use of tempo-rally-typed contents such as anamnesis and prescription free text sections results in a statistically significant improvement.

Citation (ISO format)
RUCH, Patrick et al. From episodes of care to diagnosis codes: automatic text categorization for medico-economic encoding. In: AMIA ... Annual Symposium proceedings, 2008, p. 636–640.
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ISSN of the journal1559-4076
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Technical informations

Creation09/21/2009 12:13:00 PM
First validation09/21/2009 12:13:00 PM
Update time03/14/2023 3:11:45 PM
Status update03/14/2023 3:11:45 PM
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