Leveraging Semantics in WordNet to Facilitate the Computer-Assisted Coding of ICD-11

The International Classification of Diseases (ICD) not only serves as the bedrock for health statistics but also provides a holistic overview of every health aspect of life. This study aims to facilitate the computer-assisted coding of the 11th revision of the ICD (ICD-11) by leveraging the data structures of ICD-11 and semantics in WordNet. First, a computer-assisted coding framework using WordNet and ICD-11 application programming interface (API) is proposed. Secondly, a network based on entity relations in ICD-11 and synonym sets in WordNet, called CodeNet, is developed. Thirdly, an algorithm for generating ICD-11 code candidates from CodeNet with two tuning parameters on the basis of the input of disease-related text is illustrated. Finally, the discharge summaries in the Medical Information Mart for Intensive Care III database and textual information from ICD-11 entities are used to evaluate the proposed method. Experimental results indicate that the proposed coding method achieves a precision of 84% and a recall of 89% relative to a precision of 65% and a recall of 81% achieved with the existing ICD-11 API. The proposed method also outperforms other methods in the literature by reducing a failure rate of up to 8% in ICD-11 coding. The proposed thresholds of similarity and percentage can be applied to tuning the performance of our method to meet different coding needs. In sum, improving the new structures of ICD-11 with the use of semantics in WordNet can help develop more reliable computer-aided coding systems for ICD-11 coders.