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Title

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

Authors
Tbahriti, Imad
Published in AMIA Annual Symposium. Proceedings. 2008, p. 636-40
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.
Identifiers
PMID: 18999206
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Research group Groupe Geissbuhler Antoine (informatique médicale) (222)
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. 636-40. https://archive-ouverte.unige.ch/unige:2726

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Deposited on : 2009-09-21

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