title: Using compound codes for automatic classification of clinical diagnoses.
abstract: Classification of diagnoses (a.k.a. coding) is the central part of current concept based medical IR systems. Some classification systems contain over 30,000 distinct codes which makes classifying clinical documents a time consuming labor intensive and error prone process. This paper presents a simple methodology for cleaning up and reusing existing manually coded diagnostic statements mainly extracted from clinical notes to build predictive models using a sparse-feature implementation of a Naïve Bayes classifier. One of the problems addressed is that diagnostic statements often contain several diagnoses and are assigned several codes resulting in a multi-class classification problem. We investigate one possible way of addressing this problem by introducing compound (multiple code) categories. We present experimental results of classifying >16,000 randomly selected diagnostic strings into 19 top level categories. A small improvement (3%) with using compound categories over simple categories indicates that using multiple code categories is a promising solution, although clearly in need of further research and refinement.
authors.affiliation: Division of Medical Informatics Research, Department of Health Sciences Research, Mayo Clinic, 200 SW First Street, Rochester, MN 55905, USA. Pakhomov.serguei @mayo.edu
authors.forename: Serguei V
authors.initials: SV
authors.lastname: Pakhomov
authors.forename: James D
authors.initials: JD
authors.lastname: Buntrock
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D001499:Bayes Theorem; D015510:Clinical Medicine / Q000145:classification*; D003933:Diagnosis; D005568:Forms and Records Control; D006801:Humans