Automated ICD-10 code assignment of nonstandard diagnoses via a two-stage framework

Abstract

An electronic medical record (EMR) is a rich source of clinical information for medical studies. Each physician usually has his or her own way to describe a patient's diagnosis. This results in many different ways to describe the same disease, which produces a large number of informal nonstandard diagnoses in EMRs. The Tenth Revision of International Classification of Diseases (ICD-10) is a medical classification list of codes for diagnoses. Automated ICD-10 code assignment of the nonstandard diagnosis is an important way to improve the quality of the medical study. However, manual coding is expensive, time-consuming and inefficient. Moreover, terminology in the standard diagnostic library comprises approximately 23,000 subcategory (6-digit) codes. Classifying the entire set of subcategory codes is extremely challenging. ICD-10 codes in the standard diagnostic library are organized hierarchically, and each category code (3-digit) relates to several or dozens of subcategory (6-digit) codes. Based on the hierarchical structure of the ICD-10 code, we propose a two-stage ICD-10 code assignment framework, which examines the entire category codes (approximately 1900) and searches the subcategory codes under the specific category code. Furthermore, since medical coding datasets are plagued with a training data sparsity issue, we introduce more supervised information to overcome this issue. Compared with the method that searches within approximately 23,000 subcategory codes, our approach requires examination of a considerably reduced number of codes. Extensive experiments show that our framework can improve the performance of the automated code assignment.

Keywords: Automated ICD-10 code assignment; Text-matching; Two-stage framework.