Development of Experience Base Ontology to Increase Competency of Semi-automated ICD-10-TM Coding System

Abstract

The objectives of this research were to create the International Classification of Diseases, 10th edition, Thai Modification - ICD-10-TM experience base ontology, to test usability of the ICD-10-TM experience base with knowledge base in a semi-automated ICD coding system, and to increase competency of the system. ICD-10-TM experience base ontology was created by collecting 4,880 anonymous patient records coded into ICD codes from 32 volunteer expert codes working in different hospitals. Data were checked for misspelling and mismatch elements and converted into experience base ontology using n-triple (N3) format of resource description framework. The semi-automated coding software could search experience base when initial searching from ICD knowledge base yielded no result. Competency of the semiautomated coding system was tested using another data set contain 14,982 diagnosis from 5,000 medical records of anonymous patients. All ICD codes produced by the semiautomated coding system were checked against the correct ICD codes validated by ICD expert coders. When the system use only ICD knowledge base for automated coding, it could find 7,142 ICD codes (47.67%), recall = 0.477, precision =0.909, but when it used ICD knowledge base with experience base search, it could find 9,283 ICD codes (61.96%), recall = 0.677, precision = 0.928. This increase ability of the system was statistical significant (paired T-test p-value = 0.008 (< 0.05). This research demonstrated a novel mechanism to use experience base ontology to enhance competency of semiautomate ICD coding system. The model of interaction between knowledge base and experience base developed in this work could be used as a basic knowledge for development of other computer systems to compute intelligence answer for complex questions as well.