论文标题
GRABQC：基于图形的自动化ICD编码的查询上下文化
GrabQC: Graph based Query Contextualization for automated ICD coding
论文作者
论文摘要
自动化医学编码是将临床记录编码为适当诊断和程序代码的一个过程，从ICD（国际疾病的国际分类）和CPT（当前程序术语）自动从标准分类法中自动使用。手动编码过程涉及从临床笔记中识别实体，然后查询遵循Medicare和Medicaid Services中心（CMS）指南的商业或非商业医疗法规信息检索（IR）系统。我们建议通过使用从临床注释自动提取的实体自动构造IR系统的查询来自动化此手动过程。 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.我们还提出了一种标记用于训练模型的数据集的方法。我们在三个不同设置的两个临床文本数据集上进行实验，以主张我们方法的有效性。实验结果表明，我们所提出的方法比所有三个设置中的基准都更好。
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.
