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Automated ICD-10 code assignment of nonstandard diagnoses via a two-stage framework
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2020-08-15 , DOI: 10.1016/j.artmed.2020.101939
Chengjie Mou 1 , Jiangtao Ren 1
Affiliation  

An electronic medical record (EMR) is a rich source of clinical information for medical studies. Each physician usually has his or her own way to describe a patient's diagnosis. This results in many different ways to describe the same disease, which produces a large number of informal nonstandard diagnoses in EMRs. The Tenth Revision of International Classification of Diseases (ICD-10) is a medical classification list of codes for diagnoses. Automated ICD-10 code assignment of the nonstandard diagnosis is an important way to improve the quality of the medical study. However, manual coding is expensive, time-consuming and inefficient. Moreover, terminology in the standard diagnostic library comprises approximately 23,000 subcategory (6-digit) codes. Classifying the entire set of subcategory codes is extremely challenging. ICD-10 codes in the standard diagnostic library are organized hierarchically, and each category code (3-digit) relates to several or dozens of subcategory (6-digit) codes. Based on the hierarchical structure of the ICD-10 code, we propose a two-stage ICD-10 code assignment framework, which examines the entire category codes (approximately 1900) and searches the subcategory codes under the specific category code. Furthermore, since medical coding datasets are plagued with a training data sparsity issue, we introduce more supervised information to overcome this issue. Compared with the method that searches within approximately 23,000 subcategory codes, our approach requires examination of a considerably reduced number of codes. Extensive experiments show that our framework can improve the performance of the automated code assignment.



中文翻译:

通过两阶段框架自动分配非标准诊断的 ICD-10 代码

电子病历 (EMR) 是医学研究的丰富临床信息来源。每个医生通常都有自己的方式来描述患者的诊断。这导致了描述同一疾病的许多不同方式,从而在 EMR 中产生了大量非正式的非标准诊断。国际疾病分类第十次修订本 (ICD-10) 是诊断代码的医学分类列表。非标准诊断的自动 ICD-10 代码分配是提高医学研究质量的重要途径。然而,手动编码昂贵、耗时且效率低下。此外,标准诊断库中的术语包含大约 23,000 个子类别(6 位)代码。对整个子类别代码集进行分类极具挑战性。标准诊断库中的ICD-10代码是分层组织的,每个类别代码(3位)对应几个或几十个子类别(6位)代码。基于ICD-10代码的层次结构,我们提出了一个两阶段的ICD-10代码分配框架,它检查整个类别代码(大约1900个)并搜索特定类别代码下的子类别代码。此外,由于医学编码数据集受到训练数据稀疏问题的困扰,我们引入了更多的监督信息来克服这个问题。与在大约 23,000 个子类别代码中搜索的方法相比,我们的方法需要检查的代码数量大大减少。大量实验表明,我们的框架可以提高自动代码分配的性能。

更新日期:2020-08-15
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