ICD-10 code retrieval based on distributional semantics of diagnosis descriptions

In this paper, we propose a method for extracting ICD-10 codes from the natural language description of a patient illness complaint. The proposed method is based on distributional semantics of terms that appeared in the two natural language expressions: a patient's complaint and an ICD-10 code description. In order to locate the relevant fragment of words within a given long and noisy patient's expression, word-to-word alignment is performed before evaluating the match between a patient's complaint and an ICD code. The data set used for the preliminary study consists of 81 test patient records. For each record, the proposed system retrieves a set of codes from a total of 69,000 ICD-10 codes. Through the experimental evaluation, we found that, on average, the system was able to return 3.6 correct codes from the top 10 results. By making use of a user's interaction, the performance was further improved to suggest about four correct codes from the top 10.