GrabQC：用于自动ICD编码的基于图的查询上下文化
GrabQC: Graph based Query Contextualization for automated ICD coding
摘要：
自动医疗编码是将临床记录自动从标准分类法（如ICD（国际疾病分类）和CPT（当前程序术语））中编码为适当的诊断和程序代码的过程。人工编码过程涉及从临床笔记中识别实体，然后查询遵循医疗保险和医疗补助服务中心（CMS）指南的商业或非商业医疗代码信息检索（IR）系统。我们建议通过使用从临床笔记中自动提取的实体自动构建IR系统的查询来自动化这个手动过程。我们提出了\textbf{GrabQC}，一个基于\textbf{Gra}的\textbf{Q}和\textbf{C}上下文化方法，该方法自动从临床文本中提取查询，使用图神经网络（GNN）模型对查询进行上下文化，并使用外部IR系统获得ICD代码。我们还提出了一种标记数据集以训练模型的方法。我们在三种不同的设置中对两个临床文本数据集进行实验，以证明我们方法的有效性。实验结果表明，我们提出的方法是优于比较基线在所有三个设置。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
