Development of ICD-10-TM ontology for a semi-automated morbidity coding system in Thailand.

OBJECTIVES The International Classification of Diseases and Related Health Problems, 10th Revision, Thai Modification (ICD-10-TM) ontology is a knowledge base created from the Thai modification of the World Health Organization International Classification of Diseases and Related Health Problems, 10th Revision. The objectives of this research were to develop the ICD-10-TM ontology as a knowledge base for use in a semi-automated ICD coding system and to test the usability of this system. METHODS ICD concepts and relations were identified from a tabular list and alphabetical indexes. An ICD-10-TM ontology was defined in the resource description framework (RDF), notation-3 (N3) format. All ICD-10-TM contents available as Microsoft Word documents were transformed into N3 format using Python scripts. Final RDF files were validated by ICD experts. The ontology was implemented as a knowledge base by using a novel semi-automated ICD coding system. Evaluation of usability was performed by a survey of forty volunteer users. RESULTS The ICD-10-TM ontology consists of two main knowledge bases (a tabular list knowledge base and an index knowledge base) containing a total of 309,985 concepts and 162,092 relations. The tabular list knowledge base can be divided into an upper level ontology, which defines hierarchical relationships between 22 ICD chapters, and a lower level ontology which defines relations between chapters, blocks, categories, rubrics and basic elements (include, exclude, synonym etc.) of the ICD tabular list. The index knowledge base describes relations between keywords, modifiers in general format and a table format of the ICD index. In this research, the creation of an ICD index ontology revealed interesting findings on problems with the current ICD index structure. One problem with the current structure is that it defines conditions that complicate pregnancy and perinatal conditions on the same hierarchical level as organ system diseases. This could mislead a coding algorithm into a wrong selection of ICD code. To prevent these coding errors by an algorithm, the ICD-10-TM index structure was modified by raising conditions complicating pregnancy and perinatal conditions into a higher hierarchical level of the index knowledge base. The modified ICD-10-TM ontology was implemented as a knowledge base in semi-automated ICD-10-TM coding software. A survey of users of the software revealed a high percentage of correct results obtained from ontology searches (>95%) and user satisfaction on the usability of the ontology. CONCLUSION The ICD-10-TM ontology is the first ICD-10 ontology with a comprehensive description of all concepts and relations in an ICD-10-TM tabular list and alphabetical index. A researcher developing an automated ICD coding system should be aware of The ICD index structure and the complexity of coding processes. These coding systems are not a word matching process. ICD-10 ontology should be used as a knowledge base in The ICD coding software. It can be used to facilitate successful implementation of ICD in developing countries, especially in those countries which do not have an adequate number of competent ICD coders.