Citation

Kukafka R, Bales ME, Burkhardt A, Friedman C. J. Am. Med. Inform. Assoc. 2006; 13(5): 508-515.

Affiliation

Department of Biomedical Informatics, Columbia University in New York, NY, USA; Mailman School of Public Health, Columbia University in New York, NY, USA.

Copyright

(Copyright © 2006, American Medical Informatics Association, Publisher Elsevier Publishing)

DOI

10.1197/jamia.M2107

PMID

16799117

PMCID

PMC1561799

Abstract

OBJECTIVE The International Classification of Functioning, Disability, and Health (ICF) is designed to provide a common language and framework for describing health and health-related states. The goal of this research was to investigate human and automated coding of functional status information using the ICF framework. DESIGN We extended an existing natural language processing (NLP) system to encode rehabilitation discharge summaries according to the ICF. MEASUREMENTS We conducted a formal evaluation, comparing the coding performed by expert coders, non-expert coders, and the NLP system. RESULTS Automated coding can be used to assign codes using the ICF, with results similar to those obtained by human coders, at least for the selection of ICF code and assignment of the performance qualifier. Coders achieved high agreement on ICF code assignment. CONCLUSION This research is a key next step in the development of the ICF as a sensitive and universal classification of functional status information. It is worthwhile to continue to investigate automated ICF coding.

Language: en