Summary
Background: The International Classification of Functioning, Disability and Health (ICF) has been available as a means of coding life functions but the coding process is cumbersome due to the large number of ICF codes. In the current study, we developed ICF code selection tools to support the coding of activity and participation data recorded in domiciliary mental health care reports.
Methods: We first developed a search system to facilitate the selection of ICF codes by tracking back through codes’ conceptual trees using a directory tool. We performed a morphological analysis on the training data set to correlate nouns with the ICF codes and obtained an analysis corpus to which numerical scores representing the frequencies of associated ICF codes for each noun were assigned. Based on the obtained corpus we developed a full-text search tool, which could simplify ICF coding relative to that performed using the directory tool. We then evaluated the usefulness of the former tool on the test data set.
Results: Using the full-text search tool, correct ICF codes were recorded in the first candidate list for only 54.2% of sentences. However, correct ICF codes appeared on the combined candidate lists for 90.1% of sentences and on the top three candidate lists for 71.7%. In a specific case (General Tasks and Demands), 100% of the correct codes were included on the combined candidate lists.
Conclusion: We developed selection tools that effectively supported ICF coding, although it was impossible to fully automate ICF coding. This indicated that ICF codes could more effectively be applied to mental health care.
Keywords
ICF (International Classification of Functioning - Disability and Health) - coding - mental health - nurses’ record
