  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.
