2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (2012)
Philadelphia, USA USA
Oct. 4, 2012 to Oct. 7, 2012
Jose C. Ferrao , SIEMENS SA, Healthcare Sector, Lisbon, Portugal
Monica D. Oliveira , Center for Management Studies - Institute Superior Técnico, Technical University of Lisbon, Lisbon, Portugal
Filipe Janela , SIEMENS SA, Healthcare Sector, Lisbon, Portugal
Henrique M. G. Martins , Center for Research and Creativity in Informatics (CI), HFF, Portugal
Clinical coding is an increasingly essential process within health organizations, usually performed manually and entailing several challenges: its administrative burden, raising costs and eventual errors. To address this issue, several coding support systems have been proposed across the literature. However, these systems are based on text processing methods that may be limited by poor text quality, ambiguity and lack of annotated resources. As electronic health record systems tend to implement more structured data formats, we propose a methodology for coding support based on structured clinical data collected during inpatient care from a semi-structured electronic health record. We follow a statistical learning paradigm and investigate several building blocks of the methodology to assess the feasibility of the approach. We present and discuss preliminary results obtained with real data extracted from an Internal Medicine department and identify several measures to further develop the methodology, model performance and generaliz ability.
naïve Bayes classifier, clinical coding, electronic health record, decision tree learning
J. C. Ferrao, M. D. Oliveira, F. Janela and H. M. Martins, "Clinical coding support based on structured data stored in electronic health records," 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops(BIBMW), Philadelphia, USA USA, 2012, pp. 790-797.