GrabQC: Graph based Query Contextualization for automated ICD coding

TL;DR

GrabQC is a novel graph neural network-based method that automates ICD coding by extracting and contextualizing queries from clinical notes, improving accuracy over existing baselines.

Contribution

The paper introduces GrabQC, a graph-based query contextualization approach that automates ICD coding from clinical notes using GNNs and external IR systems, with a new dataset labeling method.

Findings

Outperforms baseline methods in all experimental setups

Effective in extracting and contextualizing queries from clinical notes

Demonstrates robustness across different datasets and configurations

Abstract

Automated medical coding is a process of codifying clinical notes to appropriate diagnosis and procedure codes automatically from the standard taxonomies such as ICD (International Classification of Diseases) and CPT (Current Procedure Terminology). The manual coding process involves the identification of entities from the clinical notes followed by querying a commercial or non-commercial medical codes Information Retrieval (IR) system that follows the Centre for Medicare and Medicaid Services (CMS) guidelines. We propose to automate this manual process by automatically constructing a query for the IR system using the entities auto-extracted from the clinical notes. We propose \textbf{GrabQC}, a \textbf{Gra}ph \textbf{b}ased \textbf{Q}uery \textbf{C}ontextualization method that automatically extracts queries from the clinical text, contextualizes the queries using a Graph Neural Network (GNN) model and obtains the ICD Codes using an external IR system. We also propose a method for labelling the dataset for training the model. We perform experiments on two datasets of clinical text in three different setups to assert the effectiveness of our approach. The experimental results show that our proposed method is better than the compared baselines in all three settings.