Machine learning and feature selection for semi-automatic ICD-9 encoding
(2010) Second Louhi Workshop on Text and Data Mining of Health Documents
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- Abstract
- This paper describes the architecture of an encoding system which aim is to be implemented as a coding help at the Cliniques universtaires Saint-Luc, a hospital in Brussels. This paper focuses on machine learning methods, more specifically, on the appropriate set of attributes to be chosen in order to optimize the results of these methods. A series of four experiments was conducted on a baseline method: Naïve Bayes with varying sets of attributes. These experiments showed that a first step consisting in the extraction of information to be coded (such as diseases, procedures, aggravating factors, etc.) is essential. It also demonstrated the importance of stemming features. Restraining the classes to categories resulted in a recall of 81.1 %.
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Medori, J., & Fairon, C. (2010). Machine learning and feature selection for semi-automatic ICD-9 encoding. Second Louhi Workshop on Text and Data Mining of Health Documents. https://hdl.handle.net/2078.5/160365