MKE-Coder: Multi-Axial Knowledge with Evidence Verification in ICD Coding for Chinese EMRs

TL;DR

This paper presents MKE-Coder, a novel framework that leverages multi-axial knowledge and evidence verification to improve automatic ICD coding for Chinese EMRs, addressing language-specific challenges and knowledge integration.

Contribution

The paper introduces a new framework that combines multi-axial knowledge extraction with evidence verification, specifically tailored for Chinese EMRs, enhancing coding accuracy and efficiency.

Findings

MKE-Coder outperforms existing methods in ICD coding accuracy.

The framework effectively filters credible evidence from EMRs.

Practical evaluation shows improved coding speed and accuracy.

Abstract

The task of automatically coding the International Classification of Diseases (ICD) in the medical field has been well-established and has received much attention. Automatic coding of the ICD in the medical field has been successful in English but faces challenges when dealing with Chinese electronic medical records (EMRs). The first issue lies in the difficulty of extracting disease code-related information from Chinese EMRs, primarily due to the concise writing style and specific internal structure of the EMRs. The second problem is that previous methods have failed to leverage the disease-based multi-axial knowledge and lack of association with the corresponding clinical evidence. This paper introduces a novel framework called MKE-Coder: Multi-axial Knowledge with Evidence verification in ICD coding for Chinese EMRs. Initially, we identify candidate codes for the diagnosis and categorize each of them into knowledge under four coding axes.Subsequently, we retrieve corresponding clinical evidence from the comprehensive content of EMRs and filter credible evidence through a scoring model. Finally, to ensure the validity of the candidate code, we propose an inference module based on the masked language modeling strategy. This module verifies that all the axis knowledge associated with the candidate code is supported by evidence and provides recommendations accordingly. To evaluate the performance of our framework, we conduct experiments using a large-scale Chinese EMR dataset collected from various hospitals. The experimental results demonstrate that MKE-Coder exhibits significant superiority in the task of automatic ICD coding based on Chinese EMRs. In the practical evaluation of our method within simulated real coding scenarios, it has been demonstrated that our approach significantly aids coders in enhancing both their coding accuracy and speed.