[论文解读] From Extreme Multi-label to Multi-class: A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention

Cansu Şen, Bingyang Ye+2 more|arXiv (Cornell University)|Feb 18, 2021

Topic Modeling参考文献 29被引用 6

一句话总结

该论文提出一种分层两阶段方法，通过首先使用基于LSTM的句子标注器识别重点句子，再利用监督注意力机制对这些句子进行ICD-10编码，将极端多标签ICD-10编码问题转化为多分类问题。该方法在强基线模型基础上，实现了23%的子集准确率提升、18%的微F1提升和15%的实例F1提升，同时通过人工标注的短语级监督增强了可解释性。

ABSTRACT

Clinical coding is the task of assigning a set of alphanumeric codes, referred to as ICD (International Classification of Diseases), to a medical event based on the context captured in a clinical narrative. The latest version of ICD, ICD-10, includes more than 70,000 codes. As this is a labor-intensive and error-prone task, automatic ICD coding of medical reports using machine learning has gained significant interest in the last decade. Existing literature has modeled this problem as a multi-label task. Nevertheless, such multi-label approach is challenging due to the extremely large label set size. Furthermore, the interpretability of the predictions is essential for the endusers (e.g., healthcare providers and insurance companies). In this paper, we propose a novel approach for automatic ICD coding by reformulating the extreme multi-label problem into a simpler multi-class problem using a hierarchical solution. We made this approach viable through extensive data collection to acquire phrase-level human coder annotations to supervise our models on learning the specific relations between the input text and predicted ICD codes. Our approach employs two independently trained networks, the sentence tagger and the ICD classifier, stacked hierarchically to predict a codeset for a medical report. The sentence tagger identifies focus sentences containing a medical event or concept relevant to an ICD coding. Using a supervised attention mechanism, the ICD classifier then assigns each focus sentence with an ICD code. The proposed approach outperforms strong baselines by large margins of 23% in subset accuracy, 18% in micro-F1, and 15% in instance based F-1. With our proposed approach, interpretability is achieved not through implicitly learned attention scores but by attributing each prediction to a particular sentence and words selected by human coders.

研究动机与目标

• 为解决极端多标签ICD-10编码的挑战，包括超过70,000个标签的庞大标签空间以及模型可解释性差的问题。
• 通过将任务重新表述为分层多分类问题，减少过度编码并提高预测准确率。
• 通过在短语级别直接使用人工编码者标注数据监督注意力机制，提升可解释性。
• 开发一个可追溯至特定句子和词语的流水线，提升临床信任度与透明度。
• 缓解无监督注意力机制的局限性，后者通常无法生成有意义或具有选择性的注意力分数。

提出的方法

• 该方法采用两阶段流水线：首先使用基于LSTM的模型识别与ICD编码相关的重点句子。
• 句子标注器通过句子级别监督进行训练，将每个句子分类为重点或非重点句子。
• 重点句子被传递给ICD分类器，该分类器使用多个LSTM和监督注意力机制来分配ICD编码。
• 监督注意力机制基于人工标注的短语级数据进行训练，确保注意力分数与临床相关术语对齐。
• 最终的编码集合通过所有重点句子预测的ICD编码的并集生成，且每个预测均可追溯至特定句子和词语。
• 系统允许对句子标注器进行微调，以控制传递给ICD分类器的句子数量，从而减少过度预测。

实验结果

研究问题

• RQ1 将极端多标签ICD-10编码重新表述为分层多分类问题，能否提升预测性能并减少过度编码？
• RQ2 短语级别的人工标注能否提升ICD编码模型中注意力机制的可解释性与准确率？
• RQ3 采用先句子标注后编码分配的两阶段流水线，是否优于端到端的多标签模型？
• RQ4 与无监督注意力机制相比，监督注意力机制在多大程度上能提供具有临床意义的解释？
• RQ5 当临床记录中存在多个相似句子时，该分层设计如何影响模型的鲁棒性？

主要发现

• 所提出的流水线在大型测试集上比强基线模型提升了23%的子集准确率、18%的微F1和15%的实例F1。
• 该模型比基线模型更少出现过度预测，且可通过调整句子标注器的敏感度进一步降低。
• 即使句子标注器错误识别了重点句子，只要该句子包含相关内容，ICD分类器仍能正确分配编码，从而保持性能。
• 监督注意力机制成功将临床相关术语（如“Melanocytic Nevus”）识别为编码预测的关键证据，与人工编码者判断一致。
• 该方法通过显式关联每个ICD编码与特定句子及人工标注的词语，实现了更优的可解释性，而无需依赖隐式学习的注意力分数。
• 在存在多个相似句子的情况下，该方法依然具有鲁棒性，因为最终编码集合通过并集生成，即使句子标注不完美，也能实现正确预测。