迈向基于树的对比学习的半结构化自动ICD编码
Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive Learning
AI总结：
本研究针对ICD自动编码中的数据稀缺和临床笔记变异性问题，提出基于树编辑距离的对比预训练和掩码部分训练策略，有效提升现有编码模型性能。
摘要：
国际疾病分类（ICD）的自动编码是一项多标签文本分类任务，涉及从临床笔记中提取疾病或程序代码。尽管应用了最先进的自然语言处理（NLP）技术，但仍存在挑战，包括由于隐私限制导致的数据可用性有限，以及由于医疗专业人员不同的书写习惯和患者不同的病理特征导致的临床笔记的高度变异性。在这项工作中，我们研究了临床笔记的半结构化性质，并提出了一种自动算法将其分割成各个部分。为了解决现有ICD编码模型在有限数据下的变异性问题，我们引入了一种基于树编辑距离的软多标签相似度度量的对比预训练方法，应用于各个部分。此外，我们设计了一种掩码部分训练策略，使ICD编码模型能够定位与ICD代码相关的部分。大量的实验结果表明，我们提出的训练策略有效提升了现有ICD编码方法的性能。
英文摘要：
Automatic coding of International Classification of Diseases (ICD) is a multi-label text categorization task that involves extracting disease or procedure codes from clinical notes. Despite the application of state-of-the-art natural language processing (NLP) techniques, there are still challenges including limited availability of data due to privacy constraints and the high variability of clinical notes caused by different writing habits of medical professionals and various pathological features of patients. In this work, we investigate the semi-structured nature of clinical notes and propose an automatic algorithm to segment them into sections. To address the variability issues in existing ICD coding models with limited data, we introduce a contrastive pre-training approach on sections using a soft multi-label similarity metric based on tree edit distance. Additionally, we design a masked section training strategy to enable ICD coding models to locate sections related to ICD codes. Extensive experimental results demonstrate that our proposed training strategies effectively enhance the performance of existing ICD coding methods.
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